From 74db141d5e1a756cad26047cc15292ce211110f5 Mon Sep 17 00:00:00 2001 From: guyk1971 Date: Wed, 1 May 2019 18:37:49 +0300 Subject: [PATCH] SAC algorithm (#282) * SAC algorithm * SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train. gym_environment - fixing an error in access to gym.spaces * Soft Actor Critic - code cleanup * code cleanup * V-head initialization fix * SAC benchmarks * SAC Documentation * typo fix * documentation fixes * documentation and version update * README typo --- README.md | 5 +- benchmarks/README.md | 1 + benchmarks/clipped_ppo/README.md | 2 +- benchmarks/ddpg/README.md | 2 +- benchmarks/sac/README.md | 48 ++ benchmarks/sac/half_cheetah_sac.png | Bin 0 -> 67180 bytes benchmarks/sac/hopper_sac.png | Bin 0 -> 99681 bytes benchmarks/sac/humanoid_sac.png | Bin 0 -> 91725 bytes benchmarks/sac/inverted_pendulum_sac.png | Bin 0 -> 50099 bytes benchmarks/sac/walker2d_sac.png | Bin 0 -> 78440 bytes docs/_images/algorithms.png | Bin 51768 -> 60448 bytes docs/_images/sac.png | Bin 0 -> 111523 bytes docs/_modules/index.html | 2 + docs/_modules/rl_coach/agents/acer_agent.html | 2 +- docs/_modules/rl_coach/agents/agent.html | 268 +++++---- .../rl_coach/agents/agent_interface.html | 387 ++++++++++++ .../agents/categorical_dqn_agent.html | 13 +- .../rl_coach/agents/clipped_ppo_agent.html | 2 + docs/_modules/rl_coach/agents/dfp_agent.html | 4 +- docs/_modules/rl_coach/agents/dqn_agent.html | 12 +- docs/_modules/rl_coach/agents/mmc_agent.html | 2 +- .../rl_coach/agents/n_step_q_agent.html | 3 + docs/_modules/rl_coach/agents/nec_agent.html | 4 +- docs/_modules/rl_coach/agents/pal_agent.html | 2 +- .../rl_coach/agents/qr_dqn_agent.html | 9 +- .../rl_coach/agents/rainbow_dqn_agent.html | 12 +- .../agents/soft_actor_critic_agent.html | 554 ++++++++++++++++++ .../agents/value_optimization_agent.html | 87 ++- docs/_modules/rl_coach/base_parameters.html | 56 +- docs/_modules/rl_coach/core_types.html | 69 ++- .../rl_coach/data_stores/nfs_data_store.html | 7 +- .../rl_coach/data_stores/s3_data_store.html | 38 +- .../environments/gym_environment.html | 32 +- .../episodic/episodic_experience_replay.html | 151 ++++- .../non_episodic/experience_replay.html | 32 +- .../kubernetes_orchestrator.html | 22 +- docs/_modules/rl_coach/spaces.html | 2 +- docs/_sources/components/agents/index.rst.txt | 1 + .../agents/policy_optimization/acer.rst.txt | 1 + .../agents/policy_optimization/sac.rst.txt | 49 ++ docs/_sources/selecting_an_algorithm.rst.txt | 8 + docs/_sources/usage.rst.txt | 3 +- docs/_static/css/custom.css | 61 -- docs/components/additional_parameters.html | 16 +- docs/components/agents/imitation/bc.html | 1 + docs/components/agents/imitation/cil.html | 1 + docs/components/agents/index.html | 45 +- docs/components/agents/other/dfp.html | 5 +- .../agents/policy_optimization/ac.html | 1 + .../agents/policy_optimization/acer.html | 12 +- .../agents/policy_optimization/cppo.html | 1 + .../agents/policy_optimization/ddpg.html | 5 +- .../agents/policy_optimization/pg.html | 1 + .../agents/policy_optimization/ppo.html | 1 + .../agents/policy_optimization/sac.html | 343 +++++++++++ .../agents/value_optimization/bs_dqn.html | 1 + .../value_optimization/categorical_dqn.html | 1 + .../agents/value_optimization/double_dqn.html | 1 + .../agents/value_optimization/dqn.html | 1 + .../value_optimization/dueling_dqn.html | 1 + .../agents/value_optimization/mmc.html | 1 + .../agents/value_optimization/n_step.html | 1 + .../agents/value_optimization/naf.html | 1 + .../agents/value_optimization/nec.html | 1 + .../agents/value_optimization/pal.html | 1 + .../agents/value_optimization/qr_dqn.html | 1 + .../agents/value_optimization/rainbow.html | 1 + docs/components/architectures/index.html | 3 +- docs/components/core_types.html | 4 +- docs/components/memories/index.html | 2 +- docs/genindex.html | 30 +- docs/objects.inv | Bin 3579 -> 3610 bytes docs/searchindex.js | 2 +- docs/selecting_an_algorithm.html | 8 + docs/test.html | 52 +- docs/usage.html | 3 +- docs_raw/source/_static/img/algorithms.png | Bin 51768 -> 60448 bytes .../source/_static/img/design_imgs/sac.png | Bin 0 -> 111523 bytes docs_raw/source/components/agents/index.rst | 1 + .../agents/policy_optimization/acer.rst | 1 + .../agents/policy_optimization/sac.rst | 49 ++ docs_raw/source/diagrams.xml | 2 +- docs_raw/source/selecting_an_algorithm.rst | 8 + img/algorithms.png | Bin 51768 -> 60448 bytes rl_coach/agents/soft_actor_critic_agent.py | 321 ++++++++++ rl_coach/architectures/head_parameters.py | 18 +- .../tensorflow_components/heads/__init__.py | 6 +- .../tensorflow_components/heads/sac_head.py | 107 ++++ .../tensorflow_components/heads/sac_q_head.py | 116 ++++ .../tensorflow_components/heads/v_head.py | 11 +- rl_coach/presets/Mujoco_SAC.py | 71 +++ setup.py | 2 +- 92 files changed, 2812 insertions(+), 402 deletions(-) create mode 100644 benchmarks/sac/README.md create mode 100644 benchmarks/sac/half_cheetah_sac.png create mode 100644 benchmarks/sac/hopper_sac.png create mode 100644 benchmarks/sac/humanoid_sac.png create mode 100644 benchmarks/sac/inverted_pendulum_sac.png create mode 100644 benchmarks/sac/walker2d_sac.png create mode 100644 docs/_images/sac.png create mode 100644 docs/_modules/rl_coach/agents/agent_interface.html create mode 100644 docs/_modules/rl_coach/agents/soft_actor_critic_agent.html create mode 100644 docs/_sources/components/agents/policy_optimization/sac.rst.txt delete mode 100644 docs/_static/css/custom.css create mode 100644 docs/components/agents/policy_optimization/sac.html create mode 100644 docs_raw/source/_static/img/design_imgs/sac.png create mode 100644 docs_raw/source/components/agents/policy_optimization/sac.rst create mode 100644 rl_coach/agents/soft_actor_critic_agent.py create mode 100644 rl_coach/architectures/tensorflow_components/heads/sac_head.py create mode 100644 rl_coach/architectures/tensorflow_components/heads/sac_q_head.py create mode 100644 rl_coach/presets/Mujoco_SAC.py diff --git a/README.md b/README.md index 9799ed5..e056995 100644 --- a/README.md +++ b/README.md @@ -25,11 +25,11 @@ coach -p CartPole_DQN -r Doom Health Gathering PyBullet Minitaur Gym Extensions Ant

-Blog posts from the Intel® AI website: * [Release 0.8.0](https://ai.intel.com/reinforcement-learning-coach-intel/) (initial release) * [Release 0.9.0](https://ai.intel.com/reinforcement-learning-coach-carla-qr-dqn/) * [Release 0.10.0](https://ai.intel.com/introducing-reinforcement-learning-coach-0-10-0/) -* [Release 0.11.0](https://ai.intel.com/rl-coach-data-science-at-scale) (current release) +* [Release 0.11.0](https://ai.intel.com/rl-coach-data-science-at-scale) +* Release 0.12.0 (current release) Contacting the Coach development team is also possible through the email [coach@intel.com](coach@intel.com) @@ -277,6 +277,7 @@ dashboard * [Clipped Proximal Policy Optimization (CPPO)](https://arxiv.org/pdf/1707.06347.pdf) | **Multi Worker Single Node** ([code](rl_coach/agents/clipped_ppo_agent.py)) * [Generalized Advantage Estimation (GAE)](https://arxiv.org/abs/1506.02438) ([code](rl_coach/agents/actor_critic_agent.py#L86)) * [Sample Efficient Actor-Critic with Experience Replay (ACER)](https://arxiv.org/abs/1611.01224) | **Multi Worker Single Node** ([code](rl_coach/agents/acer_agent.py)) +* [Soft Actor-Critic (SAC)](https://arxiv.org/abs/1801.01290) ([code](rl_coach/agents/soft_actor_critic_agent.py)) ### General Agents * [Direct Future Prediction (DFP)](https://arxiv.org/abs/1611.01779) | **Multi Worker Single Node** ([code](rl_coach/agents/dfp_agent.py)) diff --git a/benchmarks/README.md b/benchmarks/README.md index 603e882..fe22f89 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -37,6 +37,7 @@ The environments that were used for testing include: |**[ACER](acer)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Atari | | |**[Clipped PPO](clipped_ppo)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Mujoco | | |**[DDPG](ddpg)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Mujoco | | +|**[SAC](sac)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Mujoco | | |**[NEC](nec)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Atari | | |**[HER](ddpg_her)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Fetch | | |**[DFP](dfp)** | ![#ceffad](https://placehold.it/15/ceffad/000000?text=+) |Doom | Doom Battle was not verified | diff --git a/benchmarks/clipped_ppo/README.md b/benchmarks/clipped_ppo/README.md index 9eedf09..f29152a 100644 --- a/benchmarks/clipped_ppo/README.md +++ b/benchmarks/clipped_ppo/README.md @@ -1,6 +1,6 @@ # Clipped PPO -Each experiment uses 3 seeds and is trained for 10k environment steps. +Each experiment uses 3 seeds and is trained for 10M environment steps. The parameters used for Clipped PPO are the same parameters as described in the [original paper](https://arxiv.org/abs/1707.06347). ### Inverted Pendulum Clipped PPO - single worker diff --git a/benchmarks/ddpg/README.md b/benchmarks/ddpg/README.md index 0163d09..59a5cdf 100644 --- a/benchmarks/ddpg/README.md +++ b/benchmarks/ddpg/README.md @@ -1,6 +1,6 @@ # DDPG -Each experiment uses 3 seeds and is trained for 2k environment steps. +Each experiment uses 3 seeds and is trained for 2M environment steps. The parameters used for DDPG are the same parameters as described in the [original paper](https://arxiv.org/abs/1509.02971). ### Inverted Pendulum DDPG - single worker diff --git a/benchmarks/sac/README.md b/benchmarks/sac/README.md new file mode 100644 index 0000000..50e7b6e --- /dev/null +++ b/benchmarks/sac/README.md @@ -0,0 +1,48 @@ +# Soft Actor Critic + +Each experiment uses 3 seeds and is trained for 3M environment steps. +The parameters used for SAC are the same parameters as described in the [original paper](https://arxiv.org/abs/1801.01290). + +### Inverted Pendulum SAC - single worker + +```bash +coach -p Mujoco_SAC -lvl inverted_pendulum +``` + +Inverted Pendulum SAC + + +### Hopper Clipped SAC - single worker + +```bash +coach -p Mujoco_SAC -lvl hopper +``` + +Hopper SAC + + +### Half Cheetah Clipped SAC - single worker + +```bash +coach -p Mujoco_SAC -lvl half_cheetah +``` + +Half Cheetah SAC + + +### Walker 2D Clipped SAC - single worker + +```bash +coach -p Mujoco_SAC -lvl walker2d +``` + +Walker 2D SAC + + +### Humanoid Clipped SAC - single worker + +```bash +coach -p Mujoco_SAC -lvl humanoid +``` + +Humanoid SAC diff --git a/benchmarks/sac/half_cheetah_sac.png b/benchmarks/sac/half_cheetah_sac.png new file mode 100644 index 0000000000000000000000000000000000000000..00b18b54d4c11d781132c13350d75ce83a7d16be GIT binary patch literal 67180 zcmaHTby!sG*7wW+Qqo9BN+T&CT`CAjNeE z9?yB+bKY~F_q#6sP++s~z4lt`{>8c{`lX5-9?pFn5D0{)ATOg10-?2oK#(A;JHU4| zkVMZwAbOC3%o9y-qunpq-Ud*g+uJOErnpnXx?1n(EQ%gMCDS*UOcDsrN74JLdQQCgg-T&f#JM5RQ zW}*9|#9GjQ_?Uut$KlLXN6M0JcGu~gH~hiIVmNk z%)v4Y4;HWPdE97UU`17wD1E!Q*5As6K7uc{F}!}xEK&cRrhfKIZEI`mTiTf6rx2!> zEaUrFTm^HzejIb(>EARtRMhszl4^Da1+uR|}1#x6j8$e14_tnQ?V2y!yus$dkxVF~)=54ti zWZS+FRBZj740S6yFiu6sqe2izm?Bu4fEd-xp}kPQx!5K@zHbKw72 zsgM#{ZVVj}bDS`}u2scWJM2&8S&)+KNBZ<&;wcz9TRToqCZvQA_#FuYoC>#YQi0m2 zzEs#a@k`@ND>4(q$x9rDK}rJPl^DdII3c7<3<<2jg`h(W zD3|&Fytuz!i4s^_YG4Y2tw{#PaY^h+0xkrdhZMsfAyIi?XTNdvC#@${zy+Y<97~5_ 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  • rl_coach.agents.acer_agent
  • rl_coach.agents.actor_critic_agent
  • rl_coach.agents.agent
    • +
  • rl_coach.agents.agent_interface
  • rl_coach.agents.bc_agent
  • rl_coach.agents.categorical_dqn_agent
  • rl_coach.agents.cil_agent
    • @@ -195,6 +196,7 @@
  • rl_coach.agents.ppo_agent
  • rl_coach.agents.qr_dqn_agent
  • rl_coach.agents.rainbow_dqn_agent
    • +
  • rl_coach.agents.soft_actor_critic_agent
  • rl_coach.agents.value_optimization_agent
  • rl_coach.architectures.architecture
  • rl_coach.architectures.network_wrapper
    • diff --git a/docs/_modules/rl_coach/agents/acer_agent.html b/docs/_modules/rl_coach/agents/acer_agent.html index 567d82c..5fc8ae0 100644 --- a/docs/_modules/rl_coach/agents/acer_agent.html +++ b/docs/_modules/rl_coach/agents/acer_agent.html @@ -248,7 +248,7 @@ self.num_steps_between_gradient_updates = 5000 self.ratio_of_replay = 4 self.num_transitions_to_start_replay = 10000 - self.rate_for_copying_weights_to_target = 0.99 + self.rate_for_copying_weights_to_target = 0.01 self.importance_weight_truncation = 10.0 self.use_trust_region_optimization = True self.max_KL_divergence = 1.0 diff --git a/docs/_modules/rl_coach/agents/agent.html b/docs/_modules/rl_coach/agents/agent.html index e9aad93..797106c 100644 --- a/docs/_modules/rl_coach/agents/agent.html +++ b/docs/_modules/rl_coach/agents/agent.html @@ -214,6 +214,9 @@ from rl_coach.utils import Signal, force_list from rl_coach.utils import dynamic_import_and_instantiate_module_from_params from rl_coach.memories.backend.memory_impl import get_memory_backend +from rl_coach.core_types import TimeTypes +from rl_coach.off_policy_evaluators.ope_manager import OpeManager +from rl_coach.core_types import PickledReplayBuffer, CsvDataset
    [docs]class Agent(AgentInterface): @@ -222,6 +225,15 @@ :param agent_parameters: A AgentParameters class instance with all the agent parameters """ super().__init__() + # use seed + if agent_parameters.task_parameters.seed is not None: + random.seed(agent_parameters.task_parameters.seed) + np.random.seed(agent_parameters.task_parameters.seed) + else: + # we need to seed the RNG since the different processes are initialized with the same parent seed + random.seed() + np.random.seed() + self.ap = agent_parameters self.task_id = self.ap.task_parameters.task_index self.is_chief = self.task_id == 0 @@ -229,10 +241,10 @@ and self.ap.memory.shared_memory if self.shared_memory: self.shared_memory_scratchpad = self.ap.task_parameters.shared_memory_scratchpad - self.name = agent_parameters.name self.parent = parent self.parent_level_manager = None - self.full_name_id = agent_parameters.full_name_id = self.name + # TODO this needs to be sorted out. Why the duplicates for the agent's name? + self.full_name_id = agent_parameters.full_name_id = self.name = agent_parameters.name if type(agent_parameters.task_parameters) == DistributedTaskParameters: screen.log_title("Creating agent - name: {} task id: {} (may take up to 30 seconds due to " @@ -264,9 +276,17 @@ self.memory.set_memory_backend(self.memory_backend) if agent_parameters.memory.load_memory_from_file_path: - screen.log_title("Loading replay buffer from pickle. Pickle path: {}" - .format(agent_parameters.memory.load_memory_from_file_path)) - self.memory.load(agent_parameters.memory.load_memory_from_file_path) + if isinstance(agent_parameters.memory.load_memory_from_file_path, PickledReplayBuffer): + screen.log_title("Loading a pickled replay buffer. Pickled file path: {}" + .format(agent_parameters.memory.load_memory_from_file_path.filepath)) + self.memory.load_pickled(agent_parameters.memory.load_memory_from_file_path.filepath) + elif isinstance(agent_parameters.memory.load_memory_from_file_path, CsvDataset): + screen.log_title("Loading a replay buffer from a CSV file. CSV file path: {}" + .format(agent_parameters.memory.load_memory_from_file_path.filepath)) + self.memory.load_csv(agent_parameters.memory.load_memory_from_file_path) + else: + raise ValueError('Trying to load a replay buffer using an unsupported method - {}. ' + .format(agent_parameters.memory.load_memory_from_file_path)) if self.shared_memory and self.is_chief: self.shared_memory_scratchpad.add(self.memory_lookup_name, self.memory) @@ -327,6 +347,7 @@ self.total_steps_counter = 0 self.running_reward = None self.training_iteration = 0 + self.training_epoch = 0 self.last_target_network_update_step = 0 self.last_training_phase_step = 0 self.current_episode = self.ap.current_episode = 0 @@ -364,14 +385,9 @@ self.discounted_return = self.register_signal('Discounted Return') if isinstance(self.in_action_space, GoalsSpace): self.distance_from_goal = self.register_signal('Distance From Goal', dump_one_value_per_step=True) - # use seed - if self.ap.task_parameters.seed is not None: - random.seed(self.ap.task_parameters.seed) - np.random.seed(self.ap.task_parameters.seed) - else: - # we need to seed the RNG since the different processes are initialized with the same parent seed - random.seed() - np.random.seed() + + # batch rl + self.ope_manager = OpeManager() if self.ap.is_batch_rl_training else None @property def parent(self) -> 'LevelManager': @@ -408,6 +424,7 @@ format(graph_name=self.parent_level_manager.parent_graph_manager.name, level_name=self.parent_level_manager.name, agent_full_id='.'.join(self.full_name_id.split('/'))) + self.agent_logger.set_index_name(self.parent_level_manager.parent_graph_manager.time_metric.value.name) self.agent_logger.set_logger_filenames(self.ap.task_parameters.experiment_path, logger_prefix=logger_prefix, add_timestamp=True, task_id=self.task_id) if self.ap.visualization.dump_in_episode_signals: @@ -561,13 +578,17 @@ self.accumulated_shaped_rewards_across_evaluation_episodes = 0 self.num_successes_across_evaluation_episodes = 0 self.num_evaluation_episodes_completed = 0 - if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high": + + # TODO verbosity was mistakenly removed from task_parameters on release 0.11.0, need to bring it back + # if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high": + if self.ap.is_a_highest_level_agent: screen.log_title("{}: Starting evaluation phase".format(self.name)) elif ending_evaluation: # we write to the next episode, because it could be that the current episode was already written # to disk and then we won't write it again - self.agent_logger.set_current_time(self.current_episode + 1) + self.agent_logger.set_current_time(self.get_current_time() + 1) + evaluation_reward = self.accumulated_rewards_across_evaluation_episodes / self.num_evaluation_episodes_completed self.agent_logger.create_signal_value( 'Evaluation Reward', evaluation_reward) @@ -577,9 +598,11 @@ success_rate = self.num_successes_across_evaluation_episodes / self.num_evaluation_episodes_completed self.agent_logger.create_signal_value( "Success Rate", - success_rate - ) - if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high": + success_rate) + + # TODO verbosity was mistakenly removed from task_parameters on release 0.11.0, need to bring it back + # if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high": + if self.ap.is_a_highest_level_agent: screen.log_title("{}: Finished evaluation phase. Success rate = {}, Avg Total Reward = {}" .format(self.name, np.round(success_rate, 2), np.round(evaluation_reward, 2)))
    @@ -652,8 +675,11 @@ :return: None """ # log all the signals to file - self.agent_logger.set_current_time(self.current_episode) + current_time = self.get_current_time() + self.agent_logger.set_current_time(current_time) self.agent_logger.create_signal_value('Training Iter', self.training_iteration) + self.agent_logger.create_signal_value('Episode #', self.current_episode) + self.agent_logger.create_signal_value('Epoch', self.training_epoch) self.agent_logger.create_signal_value('In Heatup', int(self._phase == RunPhase.HEATUP)) self.agent_logger.create_signal_value('ER #Transitions', self.call_memory('num_transitions')) self.agent_logger.create_signal_value('ER #Episodes', self.call_memory('length')) @@ -666,12 +692,17 @@ if self._phase == RunPhase.TRAIN else np.nan) self.agent_logger.create_signal_value('Update Target Network', 0, overwrite=False) - self.agent_logger.update_wall_clock_time(self.current_episode) + self.agent_logger.update_wall_clock_time(current_time) - if self._phase != RunPhase.TEST: - self.agent_logger.create_signal_value('Evaluation Reward', np.nan, overwrite=False) - self.agent_logger.create_signal_value('Shaped Evaluation Reward', np.nan, overwrite=False) - self.agent_logger.create_signal_value('Success Rate', np.nan, overwrite=False) + # The following signals are created with meaningful values only when an evaluation phase is completed. + # Creating with default NaNs for any HEATUP/TRAIN/TEST episode which is not the last in an evaluation phase + self.agent_logger.create_signal_value('Evaluation Reward', np.nan, overwrite=False) + self.agent_logger.create_signal_value('Shaped Evaluation Reward', np.nan, overwrite=False) + self.agent_logger.create_signal_value('Success Rate', np.nan, overwrite=False) + self.agent_logger.create_signal_value('Inverse Propensity Score', np.nan, overwrite=False) + self.agent_logger.create_signal_value('Direct Method Reward', np.nan, overwrite=False) + self.agent_logger.create_signal_value('Doubly Robust', np.nan, overwrite=False) + self.agent_logger.create_signal_value('Sequential Doubly Robust', np.nan, overwrite=False) for signal in self.episode_signals: self.agent_logger.create_signal_value("{}/Mean".format(signal.name), signal.get_mean()) @@ -680,8 +711,7 @@ self.agent_logger.create_signal_value("{}/Min".format(signal.name), signal.get_min()) # dump - if self.current_episode % self.ap.visualization.dump_signals_to_csv_every_x_episodes == 0 \ - and self.current_episode > 0: + if self.current_episode % self.ap.visualization.dump_signals_to_csv_every_x_episodes == 0: self.agent_logger.dump_output_csv()
    [docs] def handle_episode_ended(self) -> None: @@ -717,10 +747,13 @@ self.total_reward_in_current_episode >= self.spaces.reward.reward_success_threshold: self.num_successes_across_evaluation_episodes += 1 - if self.ap.visualization.dump_csv: + if self.ap.visualization.dump_csv and \ + self.parent_level_manager.parent_graph_manager.time_metric == TimeTypes.EpisodeNumber: self.update_log() - if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high": + # TODO verbosity was mistakenly removed from task_parameters on release 0.11.0, need to bring it back + # if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high": + if self.ap.is_a_highest_level_agent: self.log_to_screen()
    [docs] def reset_internal_state(self) -> None: @@ -831,18 +864,25 @@ """ loss = 0 if self._should_train(): + self.training_epoch += 1 for network in self.networks.values(): network.set_is_training(True) - for training_step in range(self.ap.algorithm.num_consecutive_training_steps): - # TODO: this should be network dependent - network_parameters = list(self.ap.network_wrappers.values())[0] + # At the moment we only support a single batch size for all the networks + networks_parameters = list(self.ap.network_wrappers.values()) + assert all(net.batch_size == networks_parameters[0].batch_size for net in networks_parameters) + batch_size = networks_parameters[0].batch_size + + # we either go sequentially through the entire replay buffer in the batch RL mode, + # or sample randomly for the basic RL case. + training_schedule = self.call_memory('get_shuffled_data_generator', batch_size) if \ + self.ap.is_batch_rl_training else [self.call_memory('sample', batch_size) for _ in + range(self.ap.algorithm.num_consecutive_training_steps)] + + for batch in training_schedule: # update counters self.training_iteration += 1 - - # sample a batch and train on it - batch = self.call_memory('sample', network_parameters.batch_size) if self.pre_network_filter is not None: batch = self.pre_network_filter.filter(batch, update_internal_state=False, deep_copy=False) @@ -853,15 +893,19 @@ batch = Batch(batch) total_loss, losses, unclipped_grads = self.learn_from_batch(batch) loss += total_loss + self.unclipped_grads.add_sample(unclipped_grads) - # TODO: the learning rate decay should be done through the network instead of here + # TODO: this only deals with the main network (if exists), need to do the same for other networks + # for instance, for DDPG, the LR signal is currently not shown. Probably should be done through the + # network directly instead of here # decay learning rate - if network_parameters.learning_rate_decay_rate != 0: + if 'main' in self.ap.network_wrappers and \ + self.ap.network_wrappers['main'].learning_rate_decay_rate != 0: self.curr_learning_rate.add_sample(self.networks['main'].sess.run( self.networks['main'].online_network.current_learning_rate)) else: - self.curr_learning_rate.add_sample(network_parameters.learning_rate) + self.curr_learning_rate.add_sample(networks_parameters[0].learning_rate) if any([network.has_target for network in self.networks.values()]) \ and self._should_update_online_weights_to_target(): @@ -877,6 +921,12 @@ if self.imitation: self.log_to_screen() + if self.ap.visualization.dump_csv and \ + self.parent_level_manager.parent_graph_manager.time_metric == TimeTypes.Epoch: + # in BatchRL, or imitation learning, the agent never acts, so we have to get the stats out here. + # we dump the data out every epoch + self.update_log() + for network in self.networks.values(): network.set_is_training(False) @@ -919,10 +969,11 @@ return batches_dict
    -
    [docs] def act(self) -> ActionInfo: +
    [docs] def act(self, action: Union[None, ActionType]=None) -> ActionInfo: """ Given the agents current knowledge, decide on the next action to apply to the environment + :param action: An action to take, overriding whatever the current policy is :return: An ActionInfo object, which contains the action and any additional info from the action decision process """ if self.phase == RunPhase.TRAIN and self.ap.algorithm.num_consecutive_playing_steps.num_steps == 0: @@ -935,20 +986,28 @@ self.current_episode_steps_counter += 1 # decide on the action - if self.phase == RunPhase.HEATUP and not self.ap.algorithm.heatup_using_network_decisions: - # random action - self.last_action_info = self.spaces.action.sample_with_info() - else: - # informed action - if self.pre_network_filter is not None: - # before choosing an action, first use the pre_network_filter to filter out the current state - update_filter_internal_state = self.phase is not RunPhase.TEST - curr_state = self.run_pre_network_filter_for_inference(self.curr_state, update_filter_internal_state) - + if action is None: + if self.phase == RunPhase.HEATUP and not self.ap.algorithm.heatup_using_network_decisions: + # random action + action = self.spaces.action.sample_with_info() else: - curr_state = self.curr_state - self.last_action_info = self.choose_action(curr_state) + # informed action + if self.pre_network_filter is not None: + # before choosing an action, first use the pre_network_filter to filter out the current state + update_filter_internal_state = self.phase is not RunPhase.TEST + curr_state = self.run_pre_network_filter_for_inference(self.curr_state, update_filter_internal_state) + else: + curr_state = self.curr_state + action = self.choose_action(curr_state) + assert isinstance(action, ActionInfo) + + self.last_action_info = action + + # output filters are explicitly applied after recording self.last_action_info. This is + # because the output filters may change the representation of the action so that the agent + # can no longer use the transition in it's replay buffer. It is possible that these filters + # could be moved to the environment instead, but they are here now for historical reasons. filtered_action_info = self.output_filter.filter(self.last_action_info) return filtered_action_info
    @@ -1030,37 +1089,35 @@ # make agent specific changes to the transition if needed transition = self.update_transition_before_adding_to_replay_buffer(transition) - # merge the intrinsic reward in - if self.ap.algorithm.scale_external_reward_by_intrinsic_reward_value: - transition.reward = transition.reward * (1 + self.last_action_info.action_intrinsic_reward) - else: - transition.reward = transition.reward + self.last_action_info.action_intrinsic_reward - - # sum up the total shaped reward - self.total_shaped_reward_in_current_episode += transition.reward - self.total_reward_in_current_episode += env_response.reward - self.shaped_reward.add_sample(transition.reward) - self.reward.add_sample(env_response.reward) - # add action info to transition if type(self.parent).__name__ == 'CompositeAgent': transition.add_info(self.parent.last_action_info.__dict__) else: transition.add_info(self.last_action_info.__dict__) - # create and store the transition - if self.phase in [RunPhase.TRAIN, RunPhase.HEATUP]: - # for episodic memories we keep the transitions in a local buffer until the episode is ended. - # for regular memories we insert the transitions directly to the memory - self.current_episode_buffer.insert(transition) - if not isinstance(self.memory, EpisodicExperienceReplay) \ - and not self.ap.algorithm.store_transitions_only_when_episodes_are_terminated: - self.call_memory('store', transition) + self.total_reward_in_current_episode += env_response.reward + self.reward.add_sample(env_response.reward) - if self.ap.visualization.dump_in_episode_signals: - self.update_step_in_episode_log() + return self.observe_transition(transition)
    - return transition.game_over + def observe_transition(self, transition): + # sum up the total shaped reward + self.total_shaped_reward_in_current_episode += transition.reward + self.shaped_reward.add_sample(transition.reward) + + # create and store the transition + if self.phase in [RunPhase.TRAIN, RunPhase.HEATUP]: + # for episodic memories we keep the transitions in a local buffer until the episode is ended. + # for regular memories we insert the transitions directly to the memory + self.current_episode_buffer.insert(transition) + if not isinstance(self.memory, EpisodicExperienceReplay) \ + and not self.ap.algorithm.store_transitions_only_when_episodes_are_terminated: + self.call_memory('store', transition) + + if self.ap.visualization.dump_in_episode_signals: + self.update_step_in_episode_log() + + return transition.game_over
    [docs] def post_training_commands(self) -> None: """ @@ -1145,60 +1202,6 @@ for network in self.networks.values(): network.sync()
    - # TODO-remove - this is a temporary flow, used by the trainer worker, duplicated from observe() - need to create - # an external trainer flow reusing the existing flow and methods [e.g. observe(), step(), act()] -
    [docs] def emulate_observe_on_trainer(self, transition: Transition) -> bool: - """ - This emulates the observe using the transition obtained from the rollout worker on the training worker - in case of distributed training. - Given a response from the environment, distill the observation from it and store it for later use. - The response should be a dictionary containing the performed action, the new observation and measurements, - the reward, a game over flag and any additional information necessary. - :return: - """ - - # sum up the total shaped reward - self.total_shaped_reward_in_current_episode += transition.reward - self.total_reward_in_current_episode += transition.reward - self.shaped_reward.add_sample(transition.reward) - self.reward.add_sample(transition.reward) - - # create and store the transition - if self.phase in [RunPhase.TRAIN, RunPhase.HEATUP]: - # for episodic memories we keep the transitions in a local buffer until the episode is ended. - # for regular memories we insert the transitions directly to the memory - self.current_episode_buffer.insert(transition) - if not isinstance(self.memory, EpisodicExperienceReplay) \ - and not self.ap.algorithm.store_transitions_only_when_episodes_are_terminated: - self.call_memory('store', transition) - - if self.ap.visualization.dump_in_episode_signals: - self.update_step_in_episode_log() - - return transition.game_over
    - - # TODO-remove - this is a temporary flow, used by the trainer worker, duplicated from observe() - need to create - # an external trainer flow reusing the existing flow and methods [e.g. observe(), step(), act()] -
    [docs] def emulate_act_on_trainer(self, transition: Transition) -> ActionInfo: - """ - This emulates the act using the transition obtained from the rollout worker on the training worker - in case of distributed training. - Given the agents current knowledge, decide on the next action to apply to the environment - :return: an action and a dictionary containing any additional info from the action decision process - """ - if self.phase == RunPhase.TRAIN and self.ap.algorithm.num_consecutive_playing_steps.num_steps == 0: - # This agent never plays while training (e.g. behavioral cloning) - return None - - # count steps (only when training or if we are in the evaluation worker) - if self.phase != RunPhase.TEST or self.ap.task_parameters.evaluate_only: - self.total_steps_counter += 1 - self.current_episode_steps_counter += 1 - - self.last_action_info = transition.action - - return self.last_action_info
    - def get_success_rate(self) -> float: return self.num_successes_across_evaluation_episodes / self.num_evaluation_episodes_completed @@ -1213,7 +1216,16 @@ savers = SaverCollection() for network in self.networks.values(): savers.update(network.collect_savers(parent_path_suffix)) - return savers + return savers + + def get_current_time(self): + pass + return { + TimeTypes.EpisodeNumber: self.current_episode, + TimeTypes.TrainingIteration: self.training_iteration, + TimeTypes.EnvironmentSteps: self.total_steps_counter, + TimeTypes.WallClockTime: self.agent_logger.get_current_wall_clock_time(), + TimeTypes.Epoch: self.training_epoch}[self.parent_level_manager.parent_graph_manager.time_metric] diff --git a/docs/_modules/rl_coach/agents/agent_interface.html b/docs/_modules/rl_coach/agents/agent_interface.html new file mode 100644 index 0000000..01d966d --- /dev/null +++ b/docs/_modules/rl_coach/agents/agent_interface.html @@ -0,0 +1,387 @@ + + + + + + + + + + + rl_coach.agents.agent_interface — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    • Docs »
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    • Module code »
      • + +
    • rl_coach.agents.agent_interface
      • + + +
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    + +

    Source code for rl_coach.agents.agent_interface

    +#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union, List, Dict
+
+import numpy as np
+
+from rl_coach.core_types import EnvResponse, ActionInfo, RunPhase, PredictionType, ActionType, Transition
+from rl_coach.saver import SaverCollection
+
+
+class AgentInterface(object):
+    def __init__(self):
+        self._phase = RunPhase.HEATUP
+        self._parent = None
+        self.spaces = None
+
+    @property
+    def parent(self):
+        """
+        Get the parent class of the agent
+        :return: the current phase
+        """
+        return self._parent
+
+    @parent.setter
+    def parent(self, val):
+        """
+        Change the parent class of the agent
+        :param val: the new parent
+        :return: None
+        """
+        self._parent = val
+
+    @property
+    def phase(self) -> RunPhase:
+        """
+        Get the phase of the agent
+        :return: the current phase
+        """
+        return self._phase
+
+    @phase.setter
+    def phase(self, val: RunPhase):
+        """
+        Change the phase of the agent
+        :param val: the new phase
+        :return: None
+        """
+        self._phase = val
+
+    def reset_internal_state(self) -> None:
+        """
+        Reset the episode parameters for the agent
+        :return: None
+        """
+        raise NotImplementedError("")
+
+    def train(self) -> Union[float, List]:
+        """
+        Train the agents network
+        :return: The loss of the training
+        """
+        raise NotImplementedError("")
+
+    def act(self) -> ActionInfo:
+        """
+        Get a decision of the next action to take.
+        The action is dependent on the current state which the agent holds from resetting the environment or from
+        the observe function.
+        :return: A tuple containing the actual action and additional info on the action
+        """
+        raise NotImplementedError("")
+
+    def observe(self, env_response: EnvResponse) -> bool:
+        """
+        Gets a response from the environment.
+        Processes this information for later use. For example, create a transition and store it in memory.
+        The action info (a class containing any info the agent wants to store regarding its action decision process) is
+        stored by the agent itself when deciding on the action.
+        :param env_response: a EnvResponse containing the response from the environment
+        :return: a done signal which is based on the agent knowledge. This can be different from the done signal from
+                 the environment. For example, an agent can decide to finish the episode each time it gets some
+                 intrinsic reward
+        """
+        raise NotImplementedError("")
+
+    def save_checkpoint(self, checkpoint_prefix: str) -> None:
+        """
+        Save the model of the agent to the disk. This can contain the network parameters, the memory of the agent, etc.
+        :param checkpoint_prefix: The prefix of the checkpoint file to save
+        :return: None
+        """
+        raise NotImplementedError("")
+
+    def get_predictions(self, states: Dict, prediction_type: PredictionType) -> np.ndarray:
+        """
+        Get a prediction from the agent with regard to the requested prediction_type. If the agent cannot predict this
+        type of prediction_type, or if there is more than possible way to do so, raise a ValueException.
+        :param states:
+        :param prediction_type:
+        :return: the agent's prediction
+        """
+        raise NotImplementedError("")
+
+    def set_incoming_directive(self, action: ActionType) -> None:
+        """
+        Pass a higher level command (directive) to the agent.
+        For example, a higher level agent can set the goal of the agent.
+        :param action: the directive to pass to the agent
+        :return: None
+        """
+        raise NotImplementedError("")
+
+    def collect_savers(self, parent_path_suffix: str) -> SaverCollection:
+        """
+        Collect all of agent savers
+        :param parent_path_suffix: path suffix of the parent of the agent
+            (could be name of level manager or composite agent)
+        :return: collection of all agent savers
+        """
+        raise NotImplementedError("")
+
+    def handle_episode_ended(self) -> None:
+        """
+        Make any changes needed when each episode is ended.
+        This includes incrementing counters, updating full episode dependent values, updating logs, etc.
+        This function is called right after each episode is ended.
+
+        :return: None
+        """
+        raise NotImplementedError("")
+
+    def run_off_policy_evaluation(self) -> None:
+        """
+        Run off-policy evaluation estimators to evaluate the trained policy performance against a dataset.
+        Should only be implemented for off-policy RL algorithms.
+
+        :return: None
+        """
+        raise NotImplementedError("")
+
    + +
    + +
    + + +
    +
    + +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/categorical_dqn_agent.html b/docs/_modules/rl_coach/agents/categorical_dqn_agent.html index 667a484..0b81831 100644 --- a/docs/_modules/rl_coach/agents/categorical_dqn_agent.html +++ b/docs/_modules/rl_coach/agents/categorical_dqn_agent.html @@ -264,8 +264,8 @@ # prediction's format is (batch,actions,atoms) def get_all_q_values_for_states(self, states: StateType): if self.exploration_policy.requires_action_values(): - prediction = self.get_prediction(states) - q_values = self.distribution_prediction_to_q_values(prediction) + q_values = self.get_prediction(states, + outputs=[self.networks['main'].online_network.output_heads[0].q_values]) else: q_values = None return q_values @@ -280,11 +280,14 @@ (self.networks['main'].online_network, batch.states(network_keys)) ]) + # add Q value samples for logging + self.q_values.add_sample(self.distribution_prediction_to_q_values(TD_targets)) + # select the optimal actions for the next state target_actions = np.argmax(self.distribution_prediction_to_q_values(distributional_q_st_plus_1), axis=1) - m = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size)) + m = np.zeros((batch.size, self.z_values.size)) - batches = np.arange(self.ap.network_wrappers['main'].batch_size) + batches = np.arange(batch.size) # an alternative to the for loop. 3.7x perf improvement vs. the same code done with for looping. # only 10% speedup overall - leaving commented out as the code is not as clear. @@ -297,7 +300,7 @@ # bj_ = (tzj_ - self.z_values[0]) / (self.z_values[1] - self.z_values[0]) # u_ = (np.ceil(bj_)).astype(int) # l_ = (np.floor(bj_)).astype(int) - # m_ = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size)) + # m_ = np.zeros((batch.size, self.z_values.size)) # np.add.at(m_, [batches, l_], # np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (u_ - bj_)) # np.add.at(m_, [batches, u_], diff --git a/docs/_modules/rl_coach/agents/clipped_ppo_agent.html b/docs/_modules/rl_coach/agents/clipped_ppo_agent.html index ff0397c..05b636e 100644 --- a/docs/_modules/rl_coach/agents/clipped_ppo_agent.html +++ b/docs/_modules/rl_coach/agents/clipped_ppo_agent.html @@ -387,6 +387,8 @@ self.networks['main'].online_network.output_heads[1].likelihood_ratio, self.networks['main'].online_network.output_heads[1].clipped_likelihood_ratio] + # TODO-fixme if batch.size / self.ap.network_wrappers['main'].batch_size is not an integer, we do not train on + # some of the data for i in range(int(batch.size / self.ap.network_wrappers['main'].batch_size)): start = i * self.ap.network_wrappers['main'].batch_size end = (i + 1) * self.ap.network_wrappers['main'].batch_size diff --git a/docs/_modules/rl_coach/agents/dfp_agent.html b/docs/_modules/rl_coach/agents/dfp_agent.html index 36aca38..447c31e 100644 --- a/docs/_modules/rl_coach/agents/dfp_agent.html +++ b/docs/_modules/rl_coach/agents/dfp_agent.html @@ -326,13 +326,13 @@ network_inputs = batch.states(network_keys) network_inputs['goal'] = np.repeat(np.expand_dims(self.current_goal, 0), - self.ap.network_wrappers['main'].batch_size, axis=0) + batch.size, axis=0) # get the current outputs of the network targets = self.networks['main'].online_network.predict(network_inputs) # change the targets for the taken actions - for i in range(self.ap.network_wrappers['main'].batch_size): + for i in range(batch.size): targets[i, batch.actions()[i]] = batch[i].info['future_measurements'].flatten() result = self.networks['main'].train_and_sync_networks(network_inputs, targets) diff --git a/docs/_modules/rl_coach/agents/dqn_agent.html b/docs/_modules/rl_coach/agents/dqn_agent.html index 5024209..01cd007 100644 --- a/docs/_modules/rl_coach/agents/dqn_agent.html +++ b/docs/_modules/rl_coach/agents/dqn_agent.html @@ -250,6 +250,9 @@ def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None): super().__init__(agent_parameters, parent) + def select_actions(self, next_states, q_st_plus_1): + return np.argmax(q_st_plus_1, 1) +
    [docs] def learn_from_batch(self, batch): network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys() @@ -261,11 +264,16 @@ (self.networks['main'].online_network, batch.states(network_keys)) ]) + selected_actions = self.select_actions(batch.next_states(network_keys), q_st_plus_1) + + # add Q value samples for logging + self.q_values.add_sample(TD_targets) + # only update the action that we have actually done in this transition TD_errors = [] - for i in range(self.ap.network_wrappers['main'].batch_size): + for i in range(batch.size): new_target = batch.rewards()[i] +\ - (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * np.max(q_st_plus_1[i], 0) + (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * q_st_plus_1[i][selected_actions[i]] TD_errors.append(np.abs(new_target - TD_targets[i, batch.actions()[i]])) TD_targets[i, batch.actions()[i]] = new_target diff --git a/docs/_modules/rl_coach/agents/mmc_agent.html b/docs/_modules/rl_coach/agents/mmc_agent.html index ad10e45..ad5c0f2 100644 --- a/docs/_modules/rl_coach/agents/mmc_agent.html +++ b/docs/_modules/rl_coach/agents/mmc_agent.html @@ -245,7 +245,7 @@ total_returns = batch.n_step_discounted_rewards() - for i in range(self.ap.network_wrappers['main'].batch_size): + for i in range(batch.size): one_step_target = batch.rewards()[i] + \ (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \ q_st_plus_1[i][selected_actions[i]] diff --git a/docs/_modules/rl_coach/agents/n_step_q_agent.html b/docs/_modules/rl_coach/agents/n_step_q_agent.html index d112464..e827485 100644 --- a/docs/_modules/rl_coach/agents/n_step_q_agent.html +++ b/docs/_modules/rl_coach/agents/n_step_q_agent.html @@ -303,6 +303,9 @@ else: assert True, 'The available values for targets_horizon are: 1-Step, N-Step' + # add Q value samples for logging + self.q_values.add_sample(state_value_head_targets) + # train result = self.networks['main'].online_network.accumulate_gradients(batch.states(network_keys), [state_value_head_targets]) diff --git a/docs/_modules/rl_coach/agents/nec_agent.html b/docs/_modules/rl_coach/agents/nec_agent.html index 5f1e0f4..51d49fd 100644 --- a/docs/_modules/rl_coach/agents/nec_agent.html +++ b/docs/_modules/rl_coach/agents/nec_agent.html @@ -313,7 +313,7 @@ TD_targets = self.networks['main'].online_network.predict(batch.states(network_keys)) bootstrapped_return_from_old_policy = batch.n_step_discounted_rewards() # only update the action that we have actually done in this transition - for i in range(self.ap.network_wrappers['main'].batch_size): + for i in range(batch.size): TD_targets[i, batch.actions()[i]] = bootstrapped_return_from_old_policy[i] # set the gradients to fetch for the DND update @@ -342,7 +342,7 @@ embedding = self.networks['main'].online_network.predict( self.prepare_batch_for_inference(self.curr_state, 'main'), outputs=self.networks['main'].online_network.state_embedding) - self.current_episode_state_embeddings.append(embedding) + self.current_episode_state_embeddings.append(embedding.squeeze()) return super().act() diff --git a/docs/_modules/rl_coach/agents/pal_agent.html b/docs/_modules/rl_coach/agents/pal_agent.html index 35c8cd3..bc8799b 100644 --- a/docs/_modules/rl_coach/agents/pal_agent.html +++ b/docs/_modules/rl_coach/agents/pal_agent.html @@ -264,7 +264,7 @@ # calculate TD error TD_targets = np.copy(q_st_online) total_returns = batch.n_step_discounted_rewards() - for i in range(self.ap.network_wrappers['main'].batch_size): + for i in range(batch.size): TD_targets[i, batch.actions()[i]] = batch.rewards()[i] + \ (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \ q_st_plus_1_target[i][selected_actions[i]] diff --git a/docs/_modules/rl_coach/agents/qr_dqn_agent.html b/docs/_modules/rl_coach/agents/qr_dqn_agent.html index 568ce17..307e130 100644 --- a/docs/_modules/rl_coach/agents/qr_dqn_agent.html +++ b/docs/_modules/rl_coach/agents/qr_dqn_agent.html @@ -268,11 +268,14 @@ (self.networks['main'].online_network, batch.states(network_keys)) ]) + # add Q value samples for logging + self.q_values.add_sample(self.get_q_values(current_quantiles)) + # get the optimal actions to take for the next states target_actions = np.argmax(self.get_q_values(next_state_quantiles), axis=1) # calculate the Bellman update - batch_idx = list(range(self.ap.network_wrappers['main'].batch_size)) + batch_idx = list(range(batch.size)) TD_targets = batch.rewards(True) + (1.0 - batch.game_overs(True)) * self.ap.algorithm.discount \ * next_state_quantiles[batch_idx, target_actions] @@ -283,9 +286,9 @@ # calculate the cumulative quantile probabilities and reorder them to fit the sorted quantiles order cumulative_probabilities = np.array(range(self.ap.algorithm.atoms + 1)) / float(self.ap.algorithm.atoms) # tau_i quantile_midpoints = 0.5*(cumulative_probabilities[1:] + cumulative_probabilities[:-1]) # tau^hat_i - quantile_midpoints = np.tile(quantile_midpoints, (self.ap.network_wrappers['main'].batch_size, 1)) + quantile_midpoints = np.tile(quantile_midpoints, (batch.size, 1)) sorted_quantiles = np.argsort(current_quantiles[batch_idx, batch.actions()]) - for idx in range(self.ap.network_wrappers['main'].batch_size): + for idx in range(batch.size): quantile_midpoints[idx, :] = quantile_midpoints[idx, sorted_quantiles[idx]] # train diff --git a/docs/_modules/rl_coach/agents/rainbow_dqn_agent.html b/docs/_modules/rl_coach/agents/rainbow_dqn_agent.html index 542e9e3..601dd13 100644 --- a/docs/_modules/rl_coach/agents/rainbow_dqn_agent.html +++ b/docs/_modules/rl_coach/agents/rainbow_dqn_agent.html @@ -240,9 +240,12 @@ def __init__(self): super().__init__() self.algorithm = RainbowDQNAlgorithmParameters() + + # ParameterNoiseParameters is changing the network wrapper parameters. This line needs to be done first. + self.network_wrappers = {"main": RainbowDQNNetworkParameters()} + self.exploration = ParameterNoiseParameters(self) self.memory = PrioritizedExperienceReplayParameters() - self.network_wrappers = {"main": RainbowDQNNetworkParameters()} @property def path(self): @@ -275,11 +278,14 @@ (self.networks['main'].online_network, batch.states(network_keys)) ]) + # add Q value samples for logging + self.q_values.add_sample(self.distribution_prediction_to_q_values(TD_targets)) + # only update the action that we have actually done in this transition (using the Double-DQN selected actions) target_actions = ddqn_selected_actions - m = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size)) + m = np.zeros((batch.size, self.z_values.size)) - batches = np.arange(self.ap.network_wrappers['main'].batch_size) + batches = np.arange(batch.size) for j in range(self.z_values.size): # we use batch.info('should_bootstrap_next_state') instead of (1 - batch.game_overs()) since with n-step, # we will not bootstrap for the last n-step transitions in the episode diff --git a/docs/_modules/rl_coach/agents/soft_actor_critic_agent.html b/docs/_modules/rl_coach/agents/soft_actor_critic_agent.html new file mode 100644 index 0000000..33668b2 --- /dev/null +++ b/docs/_modules/rl_coach/agents/soft_actor_critic_agent.html @@ -0,0 +1,554 @@ + + + + + + + + + + + rl_coach.agents.soft_actor_critic_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + +

    Source code for rl_coach.agents.soft_actor_critic_agent

    +#
+# Copyright (c) 2019 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+import copy
+import numpy as np
+from collections import OrderedDict
+
+from rl_coach.agents.agent import Agent
+from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
+
+from rl_coach.architectures.head_parameters import SACQHeadParameters,SACPolicyHeadParameters,VHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, EmbedderScheme, MiddlewareScheme
+from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.spaces import BoxActionSpace
+
+
+# There are 3 networks in SAC implementation. All have the same topology but parameters are not shared.
+# The networks are:
+# 1. State Value Network - SACValueNetwork
+# 2. Soft Q Value Network - SACCriticNetwork
+# 3. Policy Network - SACPolicyNetwork - currently supporting only Gaussian Policy
+
+
+# 1. State Value Network - SACValueNetwork
+# this is the state value network in SAC.
+# The network is trained to predict (regression) the state value in the max-entropy settings
+# The objective to be minimized is given in equation (5) in the paper:
+#
+# J(psi)= E_(s~D)[0.5*(V_psi(s)-y(s))^2]
+# where y(s) = E_(a~pi)[Q_theta(s,a)-log(pi(a|s))]
+
+
+# Default parameters for value network:
+# topology :
+#   input embedder : EmbedderScheme.Medium (Dense(256)) , relu activation
+#   middleware : EmbedderScheme.Medium (Dense(256)) , relu activation
+
+
+class SACValueNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='relu')}
+        self.middleware_parameters = FCMiddlewareParameters(activation_function='relu')
+        self.heads_parameters = [VHeadParameters(initializer='xavier')]
+        self.rescale_gradient_from_head_by_factor = [1]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 256
+        self.async_training = False
+        self.learning_rate = 0.0003     # 3e-4 see appendix D in the paper
+        self.create_target_network = True   # tau is set in SoftActorCriticAlgorithmParameters.rate_for_copying_weights_to_target
+
+
+# 2. Soft Q Value Network - SACCriticNetwork
+# the whole network is built in the SACQHeadParameters. we use empty input embedder and middleware
+class SACCriticNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(scheme=EmbedderScheme.Empty)}
+        self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Empty)
+        self.heads_parameters = [SACQHeadParameters()]      # SACQHeadParameters includes the topology of the head
+        self.rescale_gradient_from_head_by_factor = [1]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 256
+        self.async_training = False
+        self.learning_rate = 0.0003
+        self.create_target_network = False
+
+
+# 3. policy Network
+# Default parameters for policy network:
+# topology :
+#   input embedder : EmbedderScheme.Medium (Dense(256)) , relu activation
+#   middleware : EmbedderScheme = [Dense(256)] , relu activation --> scheme should be overridden in preset
+class SACPolicyNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='relu')}
+        self.middleware_parameters = FCMiddlewareParameters(activation_function='relu')
+        self.heads_parameters = [SACPolicyHeadParameters()]
+        self.rescale_gradient_from_head_by_factor = [1]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 256
+        self.async_training = False
+        self.learning_rate = 0.0003
+        self.create_target_network = False
+        self.l2_regularization = 0      # weight decay regularization. not used in the original paper
+
+
+# Algorithm Parameters
+
+
    [docs]class SoftActorCriticAlgorithmParameters(AlgorithmParameters):
+    """
+    :param num_steps_between_copying_online_weights_to_target: (StepMethod)
+        The number of steps between copying the online network weights to the target network weights.
+
+    :param rate_for_copying_weights_to_target: (float)
+        When copying the online network weights to the target network weights, a soft update will be used, which
+        weight the new online network weights by rate_for_copying_weights_to_target. (Tau as defined in the paper)
+
+    :param use_deterministic_for_evaluation: (bool)
+        If True, during the evaluation phase, action are chosen deterministically according to the policy mean
+        and not sampled from the policy distribution.
+    """
+    def __init__(self):
+        super().__init__()
+        self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1)
+        self.rate_for_copying_weights_to_target = 0.005
+        self.use_deterministic_for_evaluation = True    # evaluate agent using deterministic policy (i.e. take the mean value)
    
+
+
+class SoftActorCriticAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=SoftActorCriticAlgorithmParameters(),
+                         exploration=AdditiveNoiseParameters(),
+                         memory=ExperienceReplayParameters(),   # SAC doesnt use episodic related data
+                         # network wrappers:
+                         networks=OrderedDict([("policy", SACPolicyNetworkParameters()),
+                                               ("q", SACCriticNetworkParameters()),
+                                               ("v", SACValueNetworkParameters())]))
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.soft_actor_critic_agent:SoftActorCriticAgent'
+
+
+# Soft Actor Critic - https://arxiv.org/abs/1801.01290
+class SoftActorCriticAgent(PolicyOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.last_gradient_update_step_idx = 0
+
+        # register signals to track (in learn_from_batch)
+        self.policy_means = self.register_signal('Policy_mu_avg')
+        self.policy_logsig = self.register_signal('Policy_logsig')
+        self.policy_logprob_sampled = self.register_signal('Policy_logp_sampled')
+        self.policy_grads = self.register_signal('Policy_grads_sumabs')
+
+        self.q1_values = self.register_signal("Q1")
+        self.TD_err1 = self.register_signal("TD err1")
+        self.q2_values = self.register_signal("Q2")
+        self.TD_err2 = self.register_signal("TD err2")
+        self.v_tgt_ns = self.register_signal('V_tgt_ns')
+        self.v_onl_ys = self.register_signal('V_onl_ys')
+        self.action_signal = self.register_signal("actions")
+
+    def learn_from_batch(self, batch):
+        #########################################
+        # need to update the following networks:
+        # 1. actor (policy)
+        # 2. state value (v)
+        # 3. critic (q1 and q2)
+        # 4. target network - probably already handled by V
+
+        #########################################
+        # define the networks to be used
+
+        # State Value Network
+        value_network = self.networks['v']
+        value_network_keys = self.ap.network_wrappers['v'].input_embedders_parameters.keys()
+
+        # Critic Network
+        q_network = self.networks['q'].online_network
+        q_head = q_network.output_heads[0]
+        q_network_keys = self.ap.network_wrappers['q'].input_embedders_parameters.keys()
+
+        # Actor (policy) Network
+        policy_network = self.networks['policy'].online_network
+        policy_network_keys = self.ap.network_wrappers['policy'].input_embedders_parameters.keys()
+
+        ##########################################
+        # 1. updating the actor - according to (13) in the paper
+        policy_inputs = copy.copy(batch.states(policy_network_keys))
+        policy_results = policy_network.predict(policy_inputs)
+
+        policy_mu, policy_std, sampled_raw_actions, sampled_actions, sampled_actions_logprob, \
+        sampled_actions_logprob_mean = policy_results
+
+        self.policy_means.add_sample(policy_mu)
+        self.policy_logsig.add_sample(policy_std)
+        self.policy_logprob_sampled.add_sample(sampled_actions_logprob_mean)
+
+        # get the state-action values for the replayed states and their corresponding actions from the policy
+        q_inputs = copy.copy(batch.states(q_network_keys))
+        q_inputs['output_0_0'] = sampled_actions
+        log_target = q_network.predict(q_inputs)[0].squeeze()
+
+        # log internal q values
+        q1_vals, q2_vals = q_network.predict(q_inputs, outputs=[q_head.q1_output, q_head.q2_output])
+        self.q1_values.add_sample(q1_vals)
+        self.q2_values.add_sample(q2_vals)
+
+        # calculate the gradients according to (13)
+        # get the gradients of log_prob w.r.t the weights (parameters) - indicated as phi in the paper
+        initial_feed_dict = {policy_network.gradients_weights_ph[5]: np.array(1.0)}
+        dlogp_dphi = policy_network.predict(policy_inputs,
+                                            outputs=policy_network.weighted_gradients[5],
+                                            initial_feed_dict=initial_feed_dict)
+
+        # calculate dq_da
+        dq_da = q_network.predict(q_inputs,
+                                  outputs=q_network.gradients_wrt_inputs[1]['output_0_0'])
+
+        # calculate da_dphi
+        initial_feed_dict = {policy_network.gradients_weights_ph[3]: dq_da}
+        dq_dphi = policy_network.predict(policy_inputs,
+                                         outputs=policy_network.weighted_gradients[3],
+                                         initial_feed_dict=initial_feed_dict)
+
+        # now given dlogp_dphi, dq_dphi we need to calculate the policy gradients according to (13)
+        policy_grads = [dlogp_dphi[l] - dq_dphi[l] for l in range(len(dlogp_dphi))]
+
+        # apply the gradients to policy networks
+        policy_network.apply_gradients(policy_grads)
+        grads_sumabs = np.sum([np.sum(np.abs(policy_grads[l])) for l in range(len(policy_grads))])
+        self.policy_grads.add_sample(grads_sumabs)
+
+        ##########################################
+        # 2. updating the state value online network weights
+        # done by calculating the targets for the v head according to (5) in the paper
+        # value_targets = log_targets-sampled_actions_logprob
+        value_inputs = copy.copy(batch.states(value_network_keys))
+        value_targets = log_target - sampled_actions_logprob
+
+        self.v_onl_ys.add_sample(value_targets)
+
+        # call value_network apply gradients with this target
+        value_loss = value_network.online_network.train_on_batch(value_inputs, value_targets[:,None])[0]
+
+        ##########################################
+        # 3. updating the critic (q networks)
+        # updating q networks according to (7) in the paper
+
+        # define the input to the q network: state has been already updated previously, but now we need
+        # the actions from the batch (and not those sampled by the policy)
+        q_inputs['output_0_0'] = batch.actions(len(batch.actions().shape) == 1)
+
+        # define the targets : scale_reward * reward + (1-terminal)*discount*v_target_next_state
+        # define v_target_next_state
+        value_inputs = copy.copy(batch.next_states(value_network_keys))
+        v_target_next_state = value_network.target_network.predict(value_inputs)
+        self.v_tgt_ns.add_sample(v_target_next_state)
+        # Note: reward is assumed to be rescaled by RewardRescaleFilter in the preset parameters
+        TD_targets = batch.rewards(expand_dims=True) + \
+                     (1.0 - batch.game_overs(expand_dims=True)) * self.ap.algorithm.discount * v_target_next_state
+
+        # call critic network update
+        result = q_network.train_on_batch(q_inputs, TD_targets, additional_fetches=[q_head.q1_loss, q_head.q2_loss])
+        total_loss, losses, unclipped_grads = result[:3]
+        q1_loss, q2_loss = result[3]
+        self.TD_err1.add_sample(q1_loss)
+        self.TD_err2.add_sample(q2_loss)
+
+        ##########################################
+        # 4. updating the value target network
+        # I just need to set the parameter rate_for_copying_weights_to_target in the agent parameters to be 1-tau
+        # where tau is the hyper parameter as defined in sac original implementation
+
+        return total_loss, losses, unclipped_grads
+
+    def get_prediction(self, states):
+        """
+        get the mean and stdev of the policy distribution given 'states'
+        :param states: the states for which we need to sample actions from the policy
+        :return: mean and stdev
+        """
+        tf_input_state = self.prepare_batch_for_inference(states, 'policy')
+        return self.networks['policy'].online_network.predict(tf_input_state)
+
+    def train(self):
+        # since the algorithm works with experience replay buffer (non-episodic),
+        # we cant use the policy optimization train method. we need Agent.train
+        # note that since in Agent.train there is no apply_gradients, we need to do it in learn from batch
+        return Agent.train(self)
+
+    def choose_action(self, curr_state):
+        """
+        choose_action - chooses the most likely action
+        if 'deterministic' - take the mean of the policy which is the prediction of the policy network.
+        else - use the exploration policy
+        :param curr_state:
+        :return: action wrapped in ActionInfo
+        """
+        if not isinstance(self.spaces.action, BoxActionSpace):
+            raise ValueError("SAC works only for continuous control problems")
+        # convert to batch so we can run it through the network
+        tf_input_state = self.prepare_batch_for_inference(curr_state, 'policy')
+        # use the online network for prediction
+        policy_network = self.networks['policy'].online_network
+        policy_head = policy_network.output_heads[0]
+        result = policy_network.predict(tf_input_state,
+                                        outputs=[policy_head.policy_mean, policy_head.actions])
+        action_mean, action_sample = result
+
+        # if using deterministic policy, take the mean values. else, use exploration policy to sample from the pdf
+        if self.phase == RunPhase.TEST and self.ap.algorithm.use_deterministic_for_evaluation:
+            action = action_mean[0]
+        else:
+            action = action_sample[0]
+
+        self.action_signal.add_sample(action)
+
+        action_info = ActionInfo(action=action)
+        return action_info
+
    + +
    + +
    + + +
    +
    + +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/value_optimization_agent.html b/docs/_modules/rl_coach/agents/value_optimization_agent.html index 02aacb4..730229c 100644 --- a/docs/_modules/rl_coach/agents/value_optimization_agent.html +++ b/docs/_modules/rl_coach/agents/value_optimization_agent.html @@ -193,16 +193,17 @@ # See the License for the specific language governing permissions and # limitations under the License. # - +from collections import OrderedDict from typing import Union import numpy as np from rl_coach.agents.agent import Agent -from rl_coach.core_types import ActionInfo, StateType +from rl_coach.core_types import ActionInfo, StateType, Batch +from rl_coach.logger import screen from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay from rl_coach.spaces import DiscreteActionSpace - +from copy import deepcopy ## This is an abstract agent - there is no learn_from_batch method ## @@ -229,8 +230,9 @@ actions_q_values = None return actions_q_values - def get_prediction(self, states): - return self.networks['main'].online_network.predict(self.prepare_batch_for_inference(states, 'main')) + def get_prediction(self, states, outputs=None): + return self.networks['main'].online_network.predict(self.prepare_batch_for_inference(states, 'main'), + outputs=outputs) def update_transition_priorities_and_get_weights(self, TD_errors, batch): # update errors in prioritized replay buffer @@ -259,10 +261,12 @@ # this is for bootstrapped dqn if type(actions_q_values) == list and len(actions_q_values) > 0: actions_q_values = self.exploration_policy.last_action_values - actions_q_values = actions_q_values.squeeze() # store the q values statistics for logging self.q_values.add_sample(actions_q_values) + + actions_q_values = actions_q_values.squeeze() + for i, q_value in enumerate(actions_q_values): self.q_value_for_action[i].add_sample(q_value) @@ -276,6 +280,77 @@ def learn_from_batch(self, batch): raise NotImplementedError("ValueOptimizationAgent is an abstract agent. Not to be used directly.") + + def run_off_policy_evaluation(self): + """ + Run the off-policy evaluation estimators to get a prediction for the performance of the current policy based on + an evaluation dataset, which was collected by another policy(ies). + :return: None + """ + assert self.ope_manager + dataset_as_episodes = self.call_memory('get_all_complete_episodes_from_to', + (self.call_memory('get_last_training_set_episode_id') + 1, + self.call_memory('num_complete_episodes'))) + if len(dataset_as_episodes) == 0: + raise ValueError('train_to_eval_ratio is too high causing the evaluation set to be empty. ' + 'Consider decreasing its value.') + + ips, dm, dr, seq_dr = self.ope_manager.evaluate( + dataset_as_episodes=dataset_as_episodes, + batch_size=self.ap.network_wrappers['main'].batch_size, + discount_factor=self.ap.algorithm.discount, + reward_model=self.networks['reward_model'].online_network, + q_network=self.networks['main'].online_network, + network_keys=list(self.ap.network_wrappers['main'].input_embedders_parameters.keys())) + + # get the estimators out to the screen + log = OrderedDict() + log['Epoch'] = self.training_epoch + log['IPS'] = ips + log['DM'] = dm + log['DR'] = dr + log['Sequential-DR'] = seq_dr + screen.log_dict(log, prefix='Off-Policy Evaluation') + + # get the estimators out to dashboard + self.agent_logger.set_current_time(self.get_current_time() + 1) + self.agent_logger.create_signal_value('Inverse Propensity Score', ips) + self.agent_logger.create_signal_value('Direct Method Reward', dm) + self.agent_logger.create_signal_value('Doubly Robust', dr) + self.agent_logger.create_signal_value('Sequential Doubly Robust', seq_dr) + + def get_reward_model_loss(self, batch: Batch): + network_keys = self.ap.network_wrappers['reward_model'].input_embedders_parameters.keys() + current_rewards_prediction_for_all_actions = self.networks['reward_model'].online_network.predict( + batch.states(network_keys)) + current_rewards_prediction_for_all_actions[range(batch.size), batch.actions()] = batch.rewards() + + return self.networks['reward_model'].train_and_sync_networks( + batch.states(network_keys), current_rewards_prediction_for_all_actions)[0] + + def improve_reward_model(self, epochs: int): + """ + Train a reward model to be used by the doubly-robust estimator + + :param epochs: The total number of epochs to use for training a reward model + :return: None + """ + batch_size = self.ap.network_wrappers['reward_model'].batch_size + + # this is fitted from the training dataset + for epoch in range(epochs): + loss = 0 + total_transitions_processed = 0 + for i, batch in enumerate(self.call_memory('get_shuffled_data_generator', batch_size)): + batch = Batch(batch) + loss += self.get_reward_model_loss(batch) + total_transitions_processed += batch.size + + log = OrderedDict() + log['Epoch'] = epoch + log['loss'] = loss / total_transitions_processed + screen.log_dict(log, prefix='Training Reward Model') +
    diff --git a/docs/_modules/rl_coach/base_parameters.html b/docs/_modules/rl_coach/base_parameters.html index 764fd8f..7f356db 100644 --- a/docs/_modules/rl_coach/base_parameters.html +++ b/docs/_modules/rl_coach/base_parameters.html @@ -205,6 +205,7 @@ from rl_coach.core_types import TrainingSteps, EnvironmentSteps, GradientClippingMethod, RunPhase, \ SelectedPhaseOnlyDumpFilter, MaxDumpFilter from rl_coach.filters.filter import NoInputFilter +from rl_coach.logger import screen class Frameworks(Enum): @@ -379,9 +380,6 @@ # distributed agents params self.share_statistics_between_workers = True - # intrinsic reward - self.scale_external_reward_by_intrinsic_reward_value = False - # n-step returns self.n_step = -1 # calculate the total return (no bootstrap, by default) @@ -470,7 +468,8 @@ batch_size=32, replace_mse_with_huber_loss=False, create_target_network=False, - tensorflow_support=True): + tensorflow_support=True, + softmax_temperature=1): """ :param force_cpu: Force the neural networks to run on the CPU even if a GPU is available @@ -553,6 +552,8 @@ online network at will. :param tensorflow_support: A flag which specifies if the network is supported by the TensorFlow framework. + :param softmax_temperature: + If a softmax is present in the network head output, use this temperature """ super().__init__() self.framework = Frameworks.tensorflow @@ -583,16 +584,19 @@ self.heads_parameters = heads_parameters self.use_separate_networks_per_head = use_separate_networks_per_head self.optimizer_type = optimizer_type + self.replace_mse_with_huber_loss = replace_mse_with_huber_loss + self.create_target_network = create_target_network + + # Framework support + self.tensorflow_support = tensorflow_support + + # Hyper-Parameter values self.optimizer_epsilon = optimizer_epsilon self.adam_optimizer_beta1 = adam_optimizer_beta1 self.adam_optimizer_beta2 = adam_optimizer_beta2 self.rms_prop_optimizer_decay = rms_prop_optimizer_decay self.batch_size = batch_size - self.replace_mse_with_huber_loss = replace_mse_with_huber_loss - self.create_target_network = create_target_network - - # Framework support - self.tensorflow_support = tensorflow_support + self.softmax_temperature = softmax_temperature class NetworkComponentParameters(Parameters): @@ -723,6 +727,7 @@ self.is_a_highest_level_agent = True self.is_a_lowest_level_agent = True self.task_parameters = None + self.is_batch_rl_training = False @property def path(self): @@ -730,18 +735,22 @@
    [docs]class TaskParameters(Parameters): - def __init__(self, framework_type: Frameworks=Frameworks.tensorflow, evaluate_only: bool=False, use_cpu: bool=False, + def __init__(self, framework_type: Frameworks=Frameworks.tensorflow, evaluate_only: int=None, use_cpu: bool=False, experiment_path='/tmp', seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None, - checkpoint_save_dir=None, export_onnx_graph: bool=False, apply_stop_condition: bool=False, - num_gpu: int=1): + checkpoint_restore_path=None, checkpoint_save_dir=None, export_onnx_graph: bool=False, + apply_stop_condition: bool=False, num_gpu: int=1): """ :param framework_type: deep learning framework type. currently only tensorflow is supported - :param evaluate_only: the task will be used only for evaluating the model + :param evaluate_only: if not None, the task will be used only for evaluating the model for the given number of steps. + A value of 0 means that task will be evaluated for an infinite number of steps. :param use_cpu: use the cpu for this task :param experiment_path: the path to the directory which will store all the experiment outputs :param seed: a seed to use for the random numbers generator :param checkpoint_save_secs: the number of seconds between each checkpoint saving - :param checkpoint_restore_dir: the directory to restore the checkpoints from + :param checkpoint_restore_dir: + [DEPECRATED - will be removed in one of the next releases - switch to checkpoint_restore_path] + the dir to restore the checkpoints from + :param checkpoint_restore_path: the path to restore the checkpoints from :param checkpoint_save_dir: the directory to store the checkpoints in :param export_onnx_graph: If set to True, this will export an onnx graph each time a checkpoint is saved :param apply_stop_condition: If set to True, this will apply the stop condition defined by reaching a target success rate @@ -753,7 +762,13 @@ self.use_cpu = use_cpu self.experiment_path = experiment_path self.checkpoint_save_secs = checkpoint_save_secs - self.checkpoint_restore_dir = checkpoint_restore_dir + if checkpoint_restore_dir: + screen.warning('TaskParameters.checkpoint_restore_dir is DEPECRATED and will be removed in one of the next ' + 'releases. Please switch to using TaskParameters.checkpoint_restore_path, with your ' + 'directory path. ') + self.checkpoint_restore_path = checkpoint_restore_dir + else: + self.checkpoint_restore_path = checkpoint_restore_path self.checkpoint_save_dir = checkpoint_save_dir self.seed = seed self.export_onnx_graph = export_onnx_graph @@ -763,13 +778,14 @@
    [docs]class DistributedTaskParameters(TaskParameters): def __init__(self, framework_type: Frameworks, parameters_server_hosts: str, worker_hosts: str, job_type: str, - task_index: int, evaluate_only: bool=False, num_tasks: int=None, + task_index: int, evaluate_only: int=None, num_tasks: int=None, num_training_tasks: int=None, use_cpu: bool=False, experiment_path=None, dnd=None, - shared_memory_scratchpad=None, seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None, + shared_memory_scratchpad=None, seed=None, checkpoint_save_secs=None, checkpoint_restore_path=None, checkpoint_save_dir=None, export_onnx_graph: bool=False, apply_stop_condition: bool=False): """ :param framework_type: deep learning framework type. currently only tensorflow is supported - :param evaluate_only: the task will be used only for evaluating the model + :param evaluate_only: if not None, the task will be used only for evaluating the model for the given number of steps. + A value of 0 means that task will be evaluated for an infinite number of steps. :param parameters_server_hosts: comma-separated list of hostname:port pairs to which the parameter servers are assigned :param worker_hosts: comma-separated list of hostname:port pairs to which the workers are assigned @@ -782,7 +798,7 @@ :param dnd: an external DND to use for NEC. This is a workaround needed for a shared DND not using the scratchpad. :param seed: a seed to use for the random numbers generator :param checkpoint_save_secs: the number of seconds between each checkpoint saving - :param checkpoint_restore_dir: the directory to restore the checkpoints from + :param checkpoint_restore_path: the path to restore the checkpoints from :param checkpoint_save_dir: the directory to store the checkpoints in :param export_onnx_graph: If set to True, this will export an onnx graph each time a checkpoint is saved :param apply_stop_condition: If set to True, this will apply the stop condition defined by reaching a target success rate @@ -790,7 +806,7 @@ """ super().__init__(framework_type=framework_type, evaluate_only=evaluate_only, use_cpu=use_cpu, experiment_path=experiment_path, seed=seed, checkpoint_save_secs=checkpoint_save_secs, - checkpoint_restore_dir=checkpoint_restore_dir, checkpoint_save_dir=checkpoint_save_dir, + checkpoint_restore_path=checkpoint_restore_path, checkpoint_save_dir=checkpoint_save_dir, export_onnx_graph=export_onnx_graph, apply_stop_condition=apply_stop_condition) self.parameters_server_hosts = parameters_server_hosts self.worker_hosts = worker_hosts diff --git a/docs/_modules/rl_coach/core_types.html b/docs/_modules/rl_coach/core_types.html index ddab0b8..8d4c633 100644 --- a/docs/_modules/rl_coach/core_types.html +++ b/docs/_modules/rl_coach/core_types.html @@ -193,9 +193,10 @@ # See the License for the specific language governing permissions and # limitations under the License. # - +from collections import namedtuple import copy +import math from enum import Enum from random import shuffle from typing import List, Union, Dict, Any, Type @@ -218,6 +219,17 @@ Measurements = 4 +Record = namedtuple('Record', ['name', 'label']) + + +class TimeTypes(Enum): + EpisodeNumber = Record(name='Episode #', label='Episode #') + TrainingIteration = Record(name='Training Iter', label='Training Iteration') + EnvironmentSteps = Record(name='Total steps', label='Total steps (per worker)') + WallClockTime = Record(name='Wall-Clock Time', label='Wall-Clock Time (minutes)') + Epoch = Record(name='Epoch', label='Epoch #') + + # step methods class StepMethod(object): @@ -232,6 +244,37 @@ def num_steps(self, val: int) -> None: self._num_steps = val + def __eq__(self, other): + return self.num_steps == other.num_steps + + def __truediv__(self, other): + """ + divide this step method with other. If other is an integer, returns an object of the same + type as self. If other is the same type of self, returns an integer. In either case, any + floating point value is rounded up under the assumption that if we are dividing Steps, we + would rather overestimate than underestimate. + """ + if isinstance(other, type(self)): + return math.ceil(self.num_steps / other.num_steps) + elif isinstance(other, int): + return type(self)(math.ceil(self.num_steps / other)) + else: + raise TypeError("cannot divide {} by {}".format(type(self), type(other))) + + def __rtruediv__(self, other): + """ + divide this step method with other. If other is an integer, returns an object of the same + type as self. If other is the same type of self, returns an integer. In either case, any + floating point value is rounded up under the assumption that if we are dividing Steps, we + would rather overestimate than underestimate. + """ + if isinstance(other, type(self)): + return math.ceil(other.num_steps / self.num_steps) + elif isinstance(other, int): + return type(self)(math.ceil(other / self.num_steps)) + else: + raise TypeError("cannot divide {} by {}".format(type(other), type(self))) + class Frames(StepMethod): def __init__(self, num_steps): @@ -429,6 +472,9 @@ .format(self.info.keys(), new_info.keys())) self.info.update(new_info) + def update_info(self, new_info: Dict[str, Any]) -> None: + self.info.update(new_info) + def __copy__(self): new_transition = type(self)() new_transition.__dict__.update(self.__dict__) @@ -510,8 +556,7 @@ """ def __init__(self, action: ActionType, all_action_probabilities: float=0, - action_value: float=0., state_value: float=0., max_action_value: float=None, - action_intrinsic_reward: float=0): + action_value: float=0., state_value: float=0., max_action_value: float=None): """ :param action: the action :param all_action_probabilities: the probability that the action was given when selecting it @@ -520,8 +565,6 @@ :param max_action_value: in case this is an action that was selected randomly, this is the value of the action that received the maximum value. if no value is given, the action is assumed to be the action with the maximum value - :param action_intrinsic_reward: can contain any intrinsic reward that the agent wants to add to this action - selection """ self.action = action self.all_action_probabilities = all_action_probabilities @@ -530,8 +573,7 @@ if not max_action_value: self.max_action_value = action_value else: - self.max_action_value = max_action_value - self.action_intrinsic_reward = action_intrinsic_reward
    + self.max_action_value = max_action_value
    [docs]class Batch(object): @@ -1047,6 +1089,19 @@ return True else: return False + + +# TODO move to a NamedTuple, once we move to Python3.6 +# https://stackoverflow.com/questions/34269772/type-hints-in-namedtuple/34269877 +class CsvDataset(object): + def __init__(self, filepath: str, is_episodic: bool = True): + self.filepath = filepath + self.is_episodic = is_episodic + + +class PickledReplayBuffer(object): + def __init__(self, filepath: str): + self.filepath = filepath
    diff --git a/docs/_modules/rl_coach/data_stores/nfs_data_store.html b/docs/_modules/rl_coach/data_stores/nfs_data_store.html index 895aa21..39a2769 100644 --- a/docs/_modules/rl_coach/data_stores/nfs_data_store.html +++ b/docs/_modules/rl_coach/data_stores/nfs_data_store.html @@ -463,7 +463,12 @@ print("Got exception: %s\n while deleting NFS PVC", e) return False - return True + return True + + def setup_checkpoint_dir(self, crd=None): + if crd: + # TODO: find a way to upload this to the deployed nfs store. + pass diff --git a/docs/_modules/rl_coach/data_stores/s3_data_store.html b/docs/_modules/rl_coach/data_stores/s3_data_store.html index 287df0f..e766a50 100644 --- a/docs/_modules/rl_coach/data_stores/s3_data_store.html +++ b/docs/_modules/rl_coach/data_stores/s3_data_store.html @@ -257,6 +257,9 @@ return True def save_to_store(self): + self._save_to_store(self.params.checkpoint_dir) + + def _save_to_store(self, checkpoint_dir): """ save_to_store() uploads the policy checkpoint, gifs and videos to the S3 data store. It reads the checkpoint state files and uploads only the latest checkpoint files to S3. It is used by the trainer in Coach when used in the distributed mode. @@ -268,24 +271,32 @@ # Acquire lock self.mc.put_object(self.params.bucket_name, SyncFiles.LOCKFILE.value, io.BytesIO(b''), 0) - state_file = CheckpointStateFile(os.path.abspath(self.params.checkpoint_dir)) + state_file = CheckpointStateFile(os.path.abspath(checkpoint_dir)) if state_file.exists(): ckpt_state = state_file.read() checkpoint_file = None - for root, dirs, files in os.walk(self.params.checkpoint_dir): + for root, dirs, files in os.walk(checkpoint_dir): for filename in files: if filename == CheckpointStateFile.checkpoint_state_filename: checkpoint_file = (root, filename) continue if filename.startswith(ckpt_state.name): abs_name = os.path.abspath(os.path.join(root, filename)) - rel_name = os.path.relpath(abs_name, self.params.checkpoint_dir) + rel_name = os.path.relpath(abs_name, checkpoint_dir) self.mc.fput_object(self.params.bucket_name, rel_name, abs_name) abs_name = os.path.abspath(os.path.join(checkpoint_file[0], checkpoint_file[1])) - rel_name = os.path.relpath(abs_name, self.params.checkpoint_dir) + rel_name = os.path.relpath(abs_name, checkpoint_dir) self.mc.fput_object(self.params.bucket_name, rel_name, abs_name) + # upload Finished if present + if os.path.exists(os.path.join(checkpoint_dir, SyncFiles.FINISHED.value)): + self.mc.put_object(self.params.bucket_name, SyncFiles.FINISHED.value, io.BytesIO(b''), 0) + + # upload Ready if present + if os.path.exists(os.path.join(checkpoint_dir, SyncFiles.TRAINER_READY.value)): + self.mc.put_object(self.params.bucket_name, SyncFiles.TRAINER_READY.value, io.BytesIO(b''), 0) + # release lock self.mc.remove_object(self.params.bucket_name, SyncFiles.LOCKFILE.value) @@ -301,6 +312,7 @@ if self.params.expt_dir and os.path.exists(os.path.join(self.params.expt_dir, 'gifs')): for filename in os.listdir(os.path.join(self.params.expt_dir, 'gifs')): self.mc.fput_object(self.params.bucket_name, filename, os.path.join(self.params.expt_dir, 'gifs', filename)) + except ResponseError as e: print("Got exception: %s\n while saving to S3", e) @@ -337,6 +349,18 @@ except Exception as e: pass + # Check if there's a ready file + objects = self.mc.list_objects_v2(self.params.bucket_name, SyncFiles.TRAINER_READY.value) + + if next(objects, None) is not None: + try: + self.mc.fget_object( + self.params.bucket_name, SyncFiles.TRAINER_READY.value, + os.path.abspath(os.path.join(self.params.checkpoint_dir, SyncFiles.TRAINER_READY.value)) + ) + except Exception as e: + pass + checkpoint_state = state_file.read() if checkpoint_state is not None: objects = self.mc.list_objects_v2(self.params.bucket_name, prefix=checkpoint_state.name, recursive=True) @@ -346,7 +370,11 @@ self.mc.fget_object(obj.bucket_name, obj.object_name, filename) except ResponseError as e: - print("Got exception: %s\n while loading from S3", e) + print("Got exception: %s\n while loading from S3", e) + + def setup_checkpoint_dir(self, crd=None): + if crd: + self._save_to_store(crd) diff --git a/docs/_modules/rl_coach/environments/gym_environment.html b/docs/_modules/rl_coach/environments/gym_environment.html index b8ce28c..5bda38e 100644 --- a/docs/_modules/rl_coach/environments/gym_environment.html +++ b/docs/_modules/rl_coach/environments/gym_environment.html @@ -480,7 +480,6 @@ random.seed(self.seed) # frame skip and max between consecutive frames - self.is_robotics_env = 'robotics' in str(self.env.unwrapped.__class__) self.is_mujoco_env = 'mujoco' in str(self.env.unwrapped.__class__) self.is_roboschool_env = 'roboschool' in str(self.env.unwrapped.__class__) self.is_atari_env = 'Atari' in str(self.env.unwrapped.__class__) @@ -501,7 +500,7 @@ self.state_space = StateSpace({}) # observations - if not isinstance(self.env.observation_space, gym.spaces.dict_space.Dict): + if not isinstance(self.env.observation_space, gym.spaces.dict.Dict): state_space = {'observation': self.env.observation_space} else: state_space = self.env.observation_space.spaces @@ -665,38 +664,11 @@ # initialize the number of lives self._update_ale_lives() - def _set_mujoco_camera(self, camera_idx: int): - """ - This function can be used to set the camera for rendering the mujoco simulator - :param camera_idx: The index of the camera to use. Should be defined in the model - :return: None - """ - if self.env.unwrapped.viewer is not None and self.env.unwrapped.viewer.cam.fixedcamid != camera_idx: - from mujoco_py.generated import const - self.env.unwrapped.viewer.cam.type = const.CAMERA_FIXED - self.env.unwrapped.viewer.cam.fixedcamid = camera_idx - - def _get_robotics_image(self): - self.env.render() - image = self.env.unwrapped._get_viewer().read_pixels(1600, 900, depth=False)[::-1, :, :] - image = scipy.misc.imresize(image, (270, 480, 3)) - return image - def _render(self): self.env.render(mode='human') - # required for setting up a fixed camera for mujoco - if self.is_mujoco_env and not self.is_roboschool_env: - self._set_mujoco_camera(0) def get_rendered_image(self): - if self.is_robotics_env: - # necessary for fetch since the rendered image is cropped to an irrelevant part of the simulator - image = self._get_robotics_image() - else: - image = self.env.render(mode='rgb_array') - # required for setting up a fixed camera for mujoco - if self.is_mujoco_env and not self.is_roboschool_env: - self._set_mujoco_camera(0) + image = self.env.render(mode='rgb_array') return image def get_target_success_rate(self) -> float: diff --git a/docs/_modules/rl_coach/memories/episodic/episodic_experience_replay.html b/docs/_modules/rl_coach/memories/episodic/episodic_experience_replay.html index 8fc2325..507e9a0 100644 --- a/docs/_modules/rl_coach/memories/episodic/episodic_experience_replay.html +++ b/docs/_modules/rl_coach/memories/episodic/episodic_experience_replay.html @@ -179,6 +179,7 @@

    Source code for rl_coach.memories.episodic.episodic_experience_replay

     #
+#
 # Copyright (c) 2017 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -193,14 +194,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+import ast
+import math
 
-from typing import List, Tuple, Union, Dict, Any
-
+import pandas as pd
+from typing import List, Tuple, Union
 import numpy as np
+import random
 
 from rl_coach.core_types import Transition, Episode
+from rl_coach.logger import screen
 from rl_coach.memories.memory import Memory, MemoryGranularity, MemoryParameters
-from rl_coach.utils import ReaderWriterLock
+from rl_coach.utils import ReaderWriterLock, ProgressBar
+from rl_coach.core_types import CsvDataset
 
 
 class EpisodicExperienceReplayParameters(MemoryParameters):
@@ -208,6 +214,7 @@
         super().__init__()
         self.max_size = (MemoryGranularity.Transitions, 1000000)
         self.n_step = -1
+        self.train_to_eval_ratio = 1  # for OPE we'll want a value < 1
 
     @property
     def path(self):
@@ -220,7 +227,9 @@
     calculations of total return and other values that depend on the sequential behavior of the transitions
     in the episode.
     """
-    def __init__(self, max_size: Tuple[MemoryGranularity, int]=(MemoryGranularity.Transitions, 1000000), n_step=-1):
+
+    def __init__(self, max_size: Tuple[MemoryGranularity, int] = (MemoryGranularity.Transitions, 1000000), n_step=-1,
+                 train_to_eval_ratio: int = 1):
         """
         :param max_size: the maximum number of transitions or episodes to hold in the memory
         """
@@ -232,8 +241,11 @@
         self._num_transitions = 0
         self._num_transitions_in_complete_episodes = 0
         self.reader_writer_lock = ReaderWriterLock()
+        self.last_training_set_episode_id = None  # used in batch-rl
+        self.last_training_set_transition_id = None  # used in batch-rl
+        self.train_to_eval_ratio = train_to_eval_ratio  # used in batch-rl
 
-    def length(self, lock: bool=False) -> int:
+    def length(self, lock: bool = False) -> int:
         """
         Get the number of episodes in the ER (even if they are not complete)
         """
@@ -255,6 +267,9 @@
     def num_transitions_in_complete_episodes(self):
         return self._num_transitions_in_complete_episodes
 
+    def get_last_training_set_episode_id(self):
+        return self.last_training_set_episode_id
+
     def sample(self, size: int, is_consecutive_transitions=False) -> List[Transition]:
         """
         Sample a batch of transitions from the replay buffer. If the requested size is larger than the number
@@ -272,7 +287,7 @@
                     batch = self._buffer[episode_idx].transitions
                 else:
                     transition_idx = np.random.randint(size, self._buffer[episode_idx].length())
-                    batch = self._buffer[episode_idx].transitions[transition_idx-size:transition_idx]
+                    batch = self._buffer[episode_idx].transitions[transition_idx - size:transition_idx]
             else:
                 transitions_idx = np.random.randint(self.num_transitions_in_complete_episodes(), size=size)
                 batch = [self.transitions[i] for i in transitions_idx]
@@ -285,6 +300,78 @@
 
         return batch
 
+    def get_episode_for_transition(self, transition: Transition) -> (int, Episode):
+        """
+        Get the episode from which that transition came from.
+        :param transition: The transition to lookup the episode for
+        :return: (Episode number, the episode) or (-1, None) if could not find a matching episode.
+        """
+
+        for i, episode in enumerate(self._buffer):
+            if transition in episode.transitions:
+                return i, episode
+        return -1, None
+
+    def shuffle_episodes(self):
+        """
+        Shuffle all the episodes in the replay buffer
+        :return:
+        """
+        random.shuffle(self._buffer)
+        self.transitions = [t for e in self._buffer for t in e.transitions]
+
+    def get_shuffled_data_generator(self, size: int) -> List[Transition]:
+        """
+        Get an generator for iterating through the shuffled replay buffer, for processing the data in epochs.
+        If the requested size is larger than the number of samples available in the replay buffer then the batch will
+        return empty. The last returned batch may be smaller than the size requested, to accommodate for all the
+        transitions in the replay buffer.
+
+        :param size: the size of the batch to return
+        :return: a batch (list) of selected transitions from the replay buffer
+        """
+        self.reader_writer_lock.lock_writing()
+        if self.last_training_set_transition_id is None:
+            if self.train_to_eval_ratio < 0 or self.train_to_eval_ratio >= 1:
+                raise ValueError('train_to_eval_ratio should be in the (0, 1] range.')
+
+            transition = self.transitions[round(self.train_to_eval_ratio * self.num_transitions_in_complete_episodes())]
+            episode_num, episode = self.get_episode_for_transition(transition)
+            self.last_training_set_episode_id = episode_num
+            self.last_training_set_transition_id = \
+                len([t for e in self.get_all_complete_episodes_from_to(0, self.last_training_set_episode_id + 1) for t in e])
+
+        shuffled_transition_indices = list(range(self.last_training_set_transition_id))
+        random.shuffle(shuffled_transition_indices)
+
+        # The last batch drawn will usually be < batch_size (=the size variable)
+        for i in range(math.ceil(len(shuffled_transition_indices) / size)):
+            sample_data = [self.transitions[j] for j in shuffled_transition_indices[i * size: (i + 1) * size]]
+            self.reader_writer_lock.release_writing()
+
+            yield sample_data
+
+    def get_all_complete_episodes_transitions(self) -> List[Transition]:
+        """
+        Get all the transitions from all the complete episodes in the buffer
+        :return: a list of transitions
+        """
+        return self.transitions[:self.num_transitions_in_complete_episodes()]
+
+    def get_all_complete_episodes(self) -> List[Episode]:
+        """
+        Get all the transitions from all the complete episodes in the buffer
+        :return: a list of transitions
+        """
+        return self.get_all_complete_episodes_from_to(0, self.num_complete_episodes())
+
+    def get_all_complete_episodes_from_to(self, start_episode_id, end_episode_id) -> List[Episode]:
+        """
+        Get all the transitions from all the complete episodes in the buffer matching the given episode range
+        :return: a list of transitions
+        """
+        return self._buffer[start_episode_id:end_episode_id]
+
     def _enforce_max_length(self) -> None:
         """
         Make sure that the size of the replay buffer does not pass the maximum size allowed.
@@ -368,7 +455,7 @@
 
         self.reader_writer_lock.release_writing_and_reading()
 
-    def store_episode(self, episode: Episode, lock: bool=True) -> None:
+    def store_episode(self, episode: Episode, lock: bool = True) -> None:
         """
         Store a new episode in the memory.
         :param episode: the new episode to store
@@ -391,7 +478,7 @@
         if lock:
             self.reader_writer_lock.release_writing_and_reading()
 
-    def get_episode(self, episode_index: int, lock: bool=True) -> Union[None, Episode]:
+    def get_episode(self, episode_index: int, lock: bool = True) -> Union[None, Episode]:
         """
         Returns the episode in the given index. If the episode does not exist, returns None instead.
         :param episode_index: the index of the episode to return
@@ -436,7 +523,7 @@
         self.reader_writer_lock.release_writing_and_reading()
 
     # for API compatibility
-    def get(self, episode_index: int, lock: bool=True) -> Union[None, Episode]:
+    def get(self, episode_index: int, lock: bool = True) -> Union[None, Episode]:
         """
         Returns the episode in the given index. If the episode does not exist, returns None instead.
         :param episode_index: the index of the episode to return
@@ -494,7 +581,51 @@
         mean = np.mean([transition.reward for transition in self.transitions])
 
         self.reader_writer_lock.release_writing()
-        return mean
    + return mean + + def load_csv(self, csv_dataset: CsvDataset) -> None: + """ + Restore the replay buffer contents from a csv file. + The csv file is assumed to include a list of transitions. + :param csv_dataset: A construct which holds the dataset parameters + """ + df = pd.read_csv(csv_dataset.filepath) + if len(df) > self.max_size[1]: + screen.warning("Warning! The number of transitions to load into the replay buffer ({}) is " + "bigger than the max size of the replay buffer ({}). The excessive transitions will " + "not be stored.".format(len(df), self.max_size[1])) + + episode_ids = df['episode_id'].unique() + progress_bar = ProgressBar(len(episode_ids)) + state_columns = [col for col in df.columns if col.startswith('state_feature')] + + for e_id in episode_ids: + progress_bar.update(e_id) + df_episode_transitions = df[df['episode_id'] == e_id] + episode = Episode() + for (_, current_transition), (_, next_transition) in zip(df_episode_transitions[:-1].iterrows(), + df_episode_transitions[1:].iterrows()): + state = np.array([current_transition[col] for col in state_columns]) + next_state = np.array([next_transition[col] for col in state_columns]) + + episode.insert( + Transition(state={'observation': state}, + action=current_transition['action'], reward=current_transition['reward'], + next_state={'observation': next_state}, game_over=False, + info={'all_action_probabilities': + ast.literal_eval(current_transition['all_action_probabilities'])})) + + # Set the last transition to end the episode + if csv_dataset.is_episodic: + episode.get_last_transition().game_over = True + + self.store_episode(episode) + + # close the progress bar + progress_bar.update(len(episode_ids)) + progress_bar.close() + + self.shuffle_episodes() diff --git a/docs/_modules/rl_coach/memories/non_episodic/experience_replay.html b/docs/_modules/rl_coach/memories/non_episodic/experience_replay.html index 735d943..6b7c6fc 100644 --- a/docs/_modules/rl_coach/memories/non_episodic/experience_replay.html +++ b/docs/_modules/rl_coach/memories/non_episodic/experience_replay.html @@ -194,10 +194,10 @@ # limitations under the License. # -from typing import List, Tuple, Union, Dict, Any +from typing import List, Tuple, Union import pickle -import sys -import time +import random +import math import numpy as np @@ -252,7 +252,6 @@ Sample a batch of transitions form the replay buffer. If the requested size is larger than the number of samples available in the replay buffer then the batch will return empty. :param size: the size of the batch to sample - :param beta: the beta parameter used for importance sampling :return: a batch (list) of selected transitions from the replay buffer """ self.reader_writer_lock.lock_writing() @@ -272,6 +271,28 @@ self.reader_writer_lock.release_writing() return batch + def get_shuffled_data_generator(self, size: int) -> List[Transition]: + """ + Get an generator for iterating through the shuffled replay buffer, for processing the data in epochs. + If the requested size is larger than the number of samples available in the replay buffer then the batch will + return empty. The last returned batch may be smaller than the size requested, to accommodate for all the + transitions in the replay buffer. + + :param size: the size of the batch to return + :return: a batch (list) of selected transitions from the replay buffer + """ + self.reader_writer_lock.lock_writing() + shuffled_transition_indices = list(range(len(self.transitions))) + random.shuffle(shuffled_transition_indices) + + # we deliberately drop some of the ending data which is left after dividing to batches of size `size` + # for i in range(math.ceil(len(shuffled_transition_indices) / size)): + for i in range(int(len(shuffled_transition_indices) / size)): + sample_data = [self.transitions[j] for j in shuffled_transition_indices[i * size: (i + 1) * size]] + self.reader_writer_lock.release_writing() + + yield sample_data + def _enforce_max_length(self) -> None: """ Make sure that the size of the replay buffer does not pass the maximum size allowed. @@ -395,7 +416,7 @@ with open(file_path, 'wb') as file: pickle.dump(self.transitions, file) - def load(self, file_path: str) -> None: + def load_pickled(self, file_path: str) -> None: """ Restore the replay buffer contents from a pickle file. The pickle file is assumed to include a list of transitions. @@ -418,6 +439,7 @@ progress_bar.update(transition_idx) progress_bar.close() + diff --git a/docs/_modules/rl_coach/orchestrators/kubernetes_orchestrator.html b/docs/_modules/rl_coach/orchestrators/kubernetes_orchestrator.html index 47babe0..166d287 100644 --- a/docs/_modules/rl_coach/orchestrators/kubernetes_orchestrator.html +++ b/docs/_modules/rl_coach/orchestrators/kubernetes_orchestrator.html @@ -298,7 +298,7 @@ self.s3_access_key = os.environ.get('ACCESS_KEY_ID') self.s3_secret_key = os.environ.get('SECRET_ACCESS_KEY') - def setup(self) -> bool: + def setup(self, crd=None) -> bool: """ Deploys the memory backend and data stores if required. """ @@ -308,6 +308,9 @@ return False if self.params.data_store_params.store_type == "nfs": self.nfs_pvc = self.data_store.get_info() + + # Upload checkpoints in checkpoint_restore_dir (if provided) to the data store + self.data_store.setup_checkpoint_dir(crd) return True def deploy_trainer(self) -> bool: @@ -321,7 +324,6 @@ trainer_params.command += ['--memory_backend_params', json.dumps(self.params.memory_backend_parameters.__dict__)] trainer_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)] - name = "{}-{}".format(trainer_params.run_type, uuid.uuid4()) if self.params.data_store_params.store_type == "nfs": @@ -346,7 +348,7 @@ name="nfs-pvc", persistent_volume_claim=self.nfs_pvc )], - restart_policy='OnFailure' + restart_policy='Never' ), ) else: @@ -365,7 +367,7 @@ metadata=k8sclient.V1ObjectMeta(labels={'app': name}), spec=k8sclient.V1PodSpec( containers=[container], - restart_policy='OnFailure' + restart_policy='Never' ), ) @@ -427,7 +429,7 @@ name="nfs-pvc", persistent_volume_claim=self.nfs_pvc )], - restart_policy='OnFailure' + restart_policy='Never' ), ) else: @@ -446,7 +448,7 @@ metadata=k8sclient.V1ObjectMeta(labels={'app': name}), spec=k8sclient.V1PodSpec( containers=[container], - restart_policy='OnFailure' + restart_policy='Never' ) ) @@ -496,7 +498,7 @@ return for pod in pods.items: - Process(target=self._tail_log_file, args=(pod.metadata.name, api_client, self.params.namespace, path)).start() + Process(target=self._tail_log_file, args=(pod.metadata.name, api_client, self.params.namespace, path), daemon=True).start() def _tail_log_file(self, pod_name, api_client, namespace, path): if not os.path.exists(path): @@ -528,7 +530,7 @@ if not pod: return - self.tail_log(pod.metadata.name, api_client) + return self.tail_log(pod.metadata.name, api_client) def tail_log(self, pod_name, corev1_api): while True: @@ -562,9 +564,9 @@ container_status.state.waiting.reason == 'CrashLoopBackOff' or \ container_status.state.waiting.reason == 'ImagePullBackOff' or \ container_status.state.waiting.reason == 'ErrImagePull': - return + return 1 if container_status.state.terminated is not None: - return + return container_status.state.terminated.exit_code def undeploy(self): """ diff --git a/docs/_modules/rl_coach/spaces.html b/docs/_modules/rl_coach/spaces.html index b38d15b..75d67e3 100644 --- a/docs/_modules/rl_coach/spaces.html +++ b/docs/_modules/rl_coach/spaces.html @@ -828,7 +828,7 @@ """ def __init__(self, state: StateSpace, - goal: ObservationSpace, + goal: Union[ObservationSpace, None], action: ActionSpace, reward: RewardSpace): self.state = state diff --git a/docs/_sources/components/agents/index.rst.txt b/docs/_sources/components/agents/index.rst.txt index 62aaf0e..476bc7a 100644 --- a/docs/_sources/components/agents/index.rst.txt +++ b/docs/_sources/components/agents/index.rst.txt @@ -21,6 +21,7 @@ A detailed description of those algorithms can be found by navigating to each of imitation/cil policy_optimization/cppo policy_optimization/ddpg + policy_optimization/sac other/dfp value_optimization/double_dqn value_optimization/dqn diff --git a/docs/_sources/components/agents/policy_optimization/acer.rst.txt b/docs/_sources/components/agents/policy_optimization/acer.rst.txt index 7808443..d16a0e7 100644 --- a/docs/_sources/components/agents/policy_optimization/acer.rst.txt +++ b/docs/_sources/components/agents/policy_optimization/acer.rst.txt @@ -38,6 +38,7 @@ Each update perform the following procedure: .. math:: \text{where} \quad \bar{\rho}_{t} = \min{\left\{c,\rho_t\right\}},\quad \rho_t=\frac{\pi (a_t \mid s_t)}{\mu (a_t \mid s_t)} 3. **Accumulate gradients:** + :math:`\bullet` **Policy gradients (with bias correction):** .. math:: \hat{g}_t^{policy} & = & \bar{\rho}_{t} \nabla \log \pi (a_t \mid s_t) [Q^{ret}(s_t,a_t) - V(s_t)] \\ diff --git a/docs/_sources/components/agents/policy_optimization/sac.rst.txt b/docs/_sources/components/agents/policy_optimization/sac.rst.txt new file mode 100644 index 0000000..418c87e --- /dev/null +++ b/docs/_sources/components/agents/policy_optimization/sac.rst.txt @@ -0,0 +1,49 @@ +Soft Actor-Critic +============ + +**Actions space:** Continuous + +**References:** `Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/sac.png + :align: center + +Algorithm Description +--------------------- + +Choosing an action - Continuous actions ++++++++++++++++++++++++++++++++++++++ + +The policy network is used in order to predict mean and log std for each action. While training, a sample is taken +from a Gaussian distribution with these mean and std values. When testing, the agent can choose deterministically +by picking the mean value or sample from a gaussian distribution like in training. + +Training the network +++++++++++++++++++++ +Start by sampling a batch :math:`B` of transitions from the experience replay. + +* To train the **Q network**, use the following targets: + + .. math:: y_t^Q=r(s_t,a_t)+\gamma \cdot V(s_{t+1}) + + The state value used in the above target is acquired by running the target state value network. + +* To train the **State Value network**, use the following targets: + + .. math:: y_t^V = \min_{i=1,2}Q_i(s_t,\tilde{a}) - log\pi (\tilde{a} \vert s),\,\,\,\, \tilde{a} \sim \pi(\cdot \vert s_t) + + The state value network is trained using a sample-based approximation of the connection between and state value and state + action values, The actions used for constructing the target are **not** sampled from the replay buffer, but rather sampled + from the current policy. + +* To train the **actor network**, use the following equation: + + .. math:: \nabla_{\theta} J \approx \nabla_{\theta} \frac{1}{\vert B \vert} \sum_{s_t\in B} \left( Q \left(s_t, \tilde{a}_\theta(s_t)\right) - log\pi_{\theta}(\tilde{a}_{\theta}(s_t)\vert s_t) \right),\,\,\,\, \tilde{a} \sim \pi(\cdot \vert s_t) + +After every training step, do a soft update of the V target network's weights from the online networks. + + +.. autoclass:: rl_coach.agents.soft_actor_critic_agent.SoftActorCriticAlgorithmParameters \ No newline at end of file diff --git a/docs/_sources/selecting_an_algorithm.rst.txt b/docs/_sources/selecting_an_algorithm.rst.txt index e56f802..e4027de 100644 --- a/docs/_sources/selecting_an_algorithm.rst.txt +++ b/docs/_sources/selecting_an_algorithm.rst.txt @@ -198,6 +198,14 @@ The algorithms are ordered by their release date in descending order. improve stability it also employs bias correction and trust region optimization techniques. +
    + + SAC +
    + Soft Actor-Critic is an algorithm which optimizes a stochastic policy in an off-policy way. + One of the key features of SAC is that it solves a maximum entropy reinforcement learning problem. +     +
    DDPG diff --git a/docs/_sources/usage.rst.txt b/docs/_sources/usage.rst.txt index c32c07f..8ff520a 100644 --- a/docs/_sources/usage.rst.txt +++ b/docs/_sources/usage.rst.txt @@ -113,7 +113,8 @@ In Coach, this can be done in two steps - .. code-block:: python - coach -p Doom_Basic_BC -cp='agent.load_memory_from_file_path=\"/replay_buffer.p\"' + from rl_coach.core_types import PickledReplayBuffer + coach -p Doom_Basic_BC -cp='agent.load_memory_from_file_path=PickledReplayBuffer(\"/replay_buffer.p\"') Visualizations diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css deleted file mode 100644 index 6ab0757..0000000 --- a/docs/_static/css/custom.css +++ /dev/null @@ -1,61 +0,0 @@ -/* Docs background */ -.wy-side-nav-search{ - background-color: #043c74; -} - -/* Mobile version */ -.wy-nav-top{ - background-color: #043c74; -} - - -.green { - color: green; -} - -.red { - color: red; -} - -.blue { - color: blue; -} - -.yellow { - color: yellow; -} - -.badge { - border: 2px; - border-style: solid; - border-color: #6C8EBF; - border-radius: 5px; - padding: 3px 15px 3px 15px; - margin: 5px; - display: inline-block; - font-weight: bold; - font-size: 16px; - background: #DAE8FC; -} - -.badge:hover { - cursor: pointer; -} - -.badge > a { - color: black; -} - -.bordered-container { - border: 0px; - border-style: solid; - border-radius: 8px; - padding: 15px; - margin-bottom: 20px; - background: #f2f2f2; -} - -.questionnaire { - font-size: 1.2em; - line-height: 1.5em; -} \ No newline at end of file diff --git a/docs/components/additional_parameters.html b/docs/components/additional_parameters.html index a5979ab..9e97da5 100644 --- a/docs/components/additional_parameters.html +++ b/docs/components/additional_parameters.html @@ -276,19 +276,22 @@ of the trace tests suite.

    TaskParameters

    -class rl_coach.base_parameters.TaskParameters(framework_type: rl_coach.base_parameters.Frameworks = <Frameworks.tensorflow: 'TensorFlow'>, evaluate_only: bool = False, use_cpu: bool = False, experiment_path='/tmp', seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None, checkpoint_save_dir=None, export_onnx_graph: bool = False, apply_stop_condition: bool = False, num_gpu: int = 1)[source]
    +class rl_coach.base_parameters.TaskParameters(framework_type: rl_coach.base_parameters.Frameworks = <Frameworks.tensorflow: 'TensorFlow'>, evaluate_only: int = None, use_cpu: bool = False, experiment_path='/tmp', seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None, checkpoint_restore_path=None, checkpoint_save_dir=None, export_onnx_graph: bool = False, apply_stop_condition: bool = False, num_gpu: int = 1)[source]
    Parameters:
    • framework_type – deep learning framework type. currently only tensorflow is supported
      • -
    • evaluate_only – the task will be used only for evaluating the model
      • +
    • evaluate_only – if not None, the task will be used only for evaluating the model for the given number of steps. +A value of 0 means that task will be evaluated for an infinite number of steps.
    • use_cpu – use the cpu for this task
    • experiment_path – the path to the directory which will store all the experiment outputs
    • seed – a seed to use for the random numbers generator
    • checkpoint_save_secs – the number of seconds between each checkpoint saving
      • -
    • checkpoint_restore_dir – the directory to restore the checkpoints from
      • +
    • checkpoint_restore_dir – [DEPECRATED - will be removed in one of the next releases - switch to checkpoint_restore_path] +the dir to restore the checkpoints from
      • +
    • checkpoint_restore_path – the path to restore the checkpoints from
    • checkpoint_save_dir – the directory to store the checkpoints in
    • export_onnx_graph – If set to True, this will export an onnx graph each time a checkpoint is saved
    • apply_stop_condition – If set to True, this will apply the stop condition defined by reaching a target success rate
      • @@ -305,14 +308,15 @@ of the trace tests suite.

      DistributedTaskParameters

      -class rl_coach.base_parameters.DistributedTaskParameters(framework_type: rl_coach.base_parameters.Frameworks, parameters_server_hosts: str, worker_hosts: str, job_type: str, task_index: int, evaluate_only: bool = False, num_tasks: int = None, num_training_tasks: int = None, use_cpu: bool = False, experiment_path=None, dnd=None, shared_memory_scratchpad=None, seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None, checkpoint_save_dir=None, export_onnx_graph: bool = False, apply_stop_condition: bool = False)[source]
      +class rl_coach.base_parameters.DistributedTaskParameters(framework_type: rl_coach.base_parameters.Frameworks, parameters_server_hosts: str, worker_hosts: str, job_type: str, task_index: int, evaluate_only: int = None, num_tasks: int = None, num_training_tasks: int = None, use_cpu: bool = False, experiment_path=None, dnd=None, shared_memory_scratchpad=None, seed=None, checkpoint_save_secs=None, checkpoint_restore_path=None, checkpoint_save_dir=None, export_onnx_graph: bool = False, apply_stop_condition: bool = False)[source]
      Parameters:
      -act() → rl_coach.core_types.ActionInfo[source]
      +act(action: Union[None, int, float, numpy.ndarray, List] = None) → rl_coach.core_types.ActionInfo[source]

      Given the agents current knowledge, decide on the next action to apply to the environment

      - + + +
      Returns:An ActionInfo object, which contains the action and any additional info from the action decision process
      Parameters:action – An action to take, overriding whatever the current policy is
      Returns:An ActionInfo object, which contains the action and any additional info from the action decision process
      @@ -357,26 +361,6 @@ for creating the network.

    -
    -
    -emulate_act_on_trainer(transition: rl_coach.core_types.Transition) → rl_coach.core_types.ActionInfo[source]
    -

    This emulates the act using the transition obtained from the rollout worker on the training worker -in case of distributed training. -Given the agents current knowledge, decide on the next action to apply to the environment -:return: an action and a dictionary containing any additional info from the action decision process

    -
    - -
    -
    -emulate_observe_on_trainer(transition: rl_coach.core_types.Transition) → bool[source]
    -

    This emulates the observe using the transition obtained from the rollout worker on the training worker -in case of distributed training. -Given a response from the environment, distill the observation from it and store it for later use. -The response should be a dictionary containing the performed action, the new observation and measurements, -the reward, a game over flag and any additional information necessary. -:return:

    -
    -
    get_predictions(states: List[Dict[str, numpy.ndarray]], prediction_type: rl_coach.core_types.PredictionType)[source]
    @@ -540,7 +524,7 @@ given observation
    -prepare_batch_for_inference(states: Union[Dict[str, numpy.ndarray], List[Dict[str, numpy.ndarray]]], network_name: str) → Dict[str, numpy.core.multiarray.array][source]
    +prepare_batch_for_inference(states: Union[Dict[str, numpy.ndarray], List[Dict[str, numpy.ndarray]]], network_name: str) → Dict[str, numpy.array][source]

    Convert curr_state into input tensors tensorflow is expecting. i.e. if we have several inputs states, stack all observations together, measurements together, etc.

    @@ -632,6 +616,21 @@ by val, and by the current phase set in self.phase.

    +
    +
    +run_off_policy_evaluation() → None
    +

    Run off-policy evaluation estimators to evaluate the trained policy performance against a dataset. +Should only be implemented for off-policy RL algorithms.

    + +++ + + + +
    Returns:None
    +
    +
    run_pre_network_filter_for_inference(state: Dict[str, numpy.ndarray], update_filter_internal_state: bool = True) → Dict[str, numpy.ndarray][source]
    diff --git a/docs/components/agents/other/dfp.html b/docs/components/agents/other/dfp.html index 33863bb..c16fdb4 100644 --- a/docs/components/agents/other/dfp.html +++ b/docs/components/agents/other/dfp.html @@ -30,7 +30,7 @@ - + @@ -114,6 +114,7 @@
  • Conditional Imitation Learning
  • Clipped Proximal Policy Optimization
  • Deep Deterministic Policy Gradient
    • +
  • Soft Actor-Critic
  • Direct Future Prediction
    • diff --git a/docs/components/agents/policy_optimization/ac.html b/docs/components/agents/policy_optimization/ac.html index ce42794..80cc33f 100644 --- a/docs/components/agents/policy_optimization/ac.html +++ b/docs/components/agents/policy_optimization/ac.html @@ -122,6 +122,7 @@
  • Conditional Imitation Learning
  • Clipped Proximal Policy Optimization
  • Deep Deterministic Policy Gradient
    • +
  • Soft Actor-Critic
  • Direct Future Prediction
  • Double DQN
  • Deep Q Networks
    • diff --git a/docs/components/agents/policy_optimization/acer.html b/docs/components/agents/policy_optimization/acer.html index b81641a..353e05e 100644 --- a/docs/components/agents/policy_optimization/acer.html +++ b/docs/components/agents/policy_optimization/acer.html @@ -122,6 +122,7 @@
  • Conditional Imitation Learning
  • Clipped Proximal Policy Optimization
  • Deep Deterministic Policy Gradient
    • +
  • Soft Actor-Critic
  • Direct Future Prediction
  • Double DQN
  • Deep Q Networks
    • @@ -247,21 +248,20 @@ and \(n\) (replay ratio) off-p \[\text{where} \quad \bar{\rho}_{t} = \min{\left\{c,\rho_t\right\}},\quad \rho_t=\frac{\pi (a_t \mid s_t)}{\mu (a_t \mid s_t)}\] -
  • -
    Accumulate gradients:
    -

    \(\bullet\) Policy gradients (with bias correction):

    +

  • Accumulate gradients:

    +
    +

    \(\bullet\) Policy gradients (with bias correction):

    \[\begin{split}\hat{g}_t^{policy} & = & \bar{\rho}_{t} \nabla \log \pi (a_t \mid s_t) [Q^{ret}(s_t,a_t) - V(s_t)] \\ & & + \mathbb{E}_{a \sim \pi} \left(\left[\frac{\rho_t(a)-c}{\rho_t(a)}\right] \nabla \log \pi (a \mid s_t) [Q(s_t,a) - V(s_t)] \right)\end{split}\]

    \(\bullet\) Q-Head gradients (MSE):

    -
    +
    \[\begin{split}\hat{g}_t^{Q} = (Q^{ret}(s_t,a_t) - Q(s_t,a_t)) \nabla Q(s_t,a_t)\\\end{split}\]
    -
    • -
    +

  • (Optional) Trust region update: change the policy loss gradient w.r.t network output:

    diff --git a/docs/components/agents/policy_optimization/cppo.html b/docs/components/agents/policy_optimization/cppo.html index 7ec56f8..dc6780f 100644 --- a/docs/components/agents/policy_optimization/cppo.html +++ b/docs/components/agents/policy_optimization/cppo.html @@ -122,6 +122,7 @@
  • Deep Deterministic Policy Gradient
    • +
  • Soft Actor-Critic
  • Direct Future Prediction
  • Double DQN
  • Deep Q Networks
    • diff --git a/docs/components/agents/policy_optimization/ddpg.html b/docs/components/agents/policy_optimization/ddpg.html index 237d2be..130b03b 100644 --- a/docs/components/agents/policy_optimization/ddpg.html +++ b/docs/components/agents/policy_optimization/ddpg.html @@ -29,7 +29,7 @@ - + @@ -122,6 +122,7 @@ +
  • Soft Actor-Critic
  • Direct Future Prediction
  • Double DQN
  • Deep Q Networks
    • @@ -297,7 +298,7 @@ values. If set to False, the terminal states reward will be taken as the target