mirror of
https://github.com/gryf/coach.git
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6e08c55ad5
allowing for the last training batch drawn to be smaller than batch_size + adding support for more agents in BatchRL by adding softmax with temperature to the corresponding heads + adding a CartPole_QR_DQN preset with a golden test + cleanups
182 lines
8.5 KiB
Python
182 lines
8.5 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from copy import deepcopy
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from typing import Tuple, List, Union
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from rl_coach.agents.dqn_agent import DQNAgentParameters
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from rl_coach.agents.nec_agent import NECAgentParameters
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from rl_coach.base_parameters import AgentParameters, VisualizationParameters, TaskParameters, \
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PresetValidationParameters
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from rl_coach.core_types import RunPhase
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from rl_coach.environments.environment import EnvironmentParameters, Environment
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from rl_coach.graph_managers.graph_manager import ScheduleParameters
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from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
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from rl_coach.level_manager import LevelManager
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from rl_coach.logger import screen
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from rl_coach.spaces import SpacesDefinition
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from rl_coach.utils import short_dynamic_import
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from rl_coach.memories.episodic import EpisodicExperienceReplayParameters
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from rl_coach.core_types import TimeTypes
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class BatchRLGraphManager(BasicRLGraphManager):
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"""
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A batch RL graph manager creates scenario of learning from a dataset without a simulator.
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"""
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def __init__(self, agent_params: AgentParameters, env_params: Union[EnvironmentParameters, None],
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schedule_params: ScheduleParameters,
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vis_params: VisualizationParameters = VisualizationParameters(),
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preset_validation_params: PresetValidationParameters = PresetValidationParameters(),
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name='batch_rl_graph', spaces_definition: SpacesDefinition = None, reward_model_num_epochs: int = 100,
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train_to_eval_ratio: float = 0.8):
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super().__init__(agent_params, env_params, schedule_params, vis_params, preset_validation_params, name)
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self.is_batch_rl = True
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self.time_metric = TimeTypes.Epoch
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self.reward_model_num_epochs = reward_model_num_epochs
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self.spaces_definition = spaces_definition
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# setting this here to make sure that, by default, train_to_eval_ratio gets a value < 1
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# (its default value in the memory is 1)
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self.agent_params.memory.train_to_eval_ratio = train_to_eval_ratio
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def _create_graph(self, task_parameters: TaskParameters) -> Tuple[List[LevelManager], List[Environment]]:
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if self.env_params:
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# environment loading
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self.env_params.seed = task_parameters.seed
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self.env_params.experiment_path = task_parameters.experiment_path
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env = short_dynamic_import(self.env_params.path)(**self.env_params.__dict__,
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visualization_parameters=self.visualization_parameters)
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else:
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env = None
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# Only DQN variants and NEC are supported at this point.
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assert(isinstance(self.agent_params, DQNAgentParameters) or isinstance(self.agent_params, NECAgentParameters))
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# Only Episodic memories are supported,
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# for evaluating the sequential doubly robust estimator
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assert(isinstance(self.agent_params.memory, EpisodicExperienceReplayParameters))
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# agent loading
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self.agent_params.task_parameters = task_parameters # TODO: this should probably be passed in a different way
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self.agent_params.name = "agent"
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self.agent_params.is_batch_rl_training = True
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# user hasn't defined params for the reward model. we will use the same params as used for the 'main' network.
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if 'reward_model' not in self.agent_params.network_wrappers:
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self.agent_params.network_wrappers['reward_model'] = deepcopy(self.agent_params.network_wrappers['main'])
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agent = short_dynamic_import(self.agent_params.path)(self.agent_params)
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if not env and not self.agent_params.memory.load_memory_from_file_path:
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screen.warning("A BatchRLGraph requires setting a dataset to load into the agent's memory or alternatively "
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"using an environment to create a (random) dataset from. This agent should only be used for "
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"inference. ")
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# set level manager
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level_manager = LevelManager(agents=agent, environment=env, name="main_level",
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spaces_definition=self.spaces_definition)
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if env:
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return [level_manager], [env]
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else:
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return [level_manager], []
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def improve(self):
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"""
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The main loop of the run.
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Defined in the following steps:
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1. Heatup
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2. Repeat:
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2.1. Repeat:
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2.1.1. Train
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2.1.2. Possibly save checkpoint
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2.2. Evaluate
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:return: None
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"""
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self.verify_graph_was_created()
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# initialize the network parameters from the global network
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self.sync()
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# TODO a bug in heatup where the last episode run is not fed into the ER. e.g. asked for 1024 heatup steps,
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# last ran episode ended increased the total to 1040 steps, but the ER will contain only 1014 steps.
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# The last episode is not there. Is this a bug in my changes or also on master?
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# Creating a dataset during the heatup phase is useful mainly for tutorial and debug purposes. If we have both
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# an environment and a dataset to load from, we will use the environment only for evaluating the policy,
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# and will not run heatup.
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# heatup
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if self.env_params is not None and not self.agent_params.memory.load_memory_from_file_path:
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self.heatup(self.heatup_steps)
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self.improve_reward_model()
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# improve
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if self.task_parameters.task_index is not None:
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screen.log_title("Starting to improve {} task index {}".format(self.name, self.task_parameters.task_index))
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else:
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screen.log_title("Starting to improve {}".format(self.name))
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# the outer most training loop
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improve_steps_end = self.total_steps_counters[RunPhase.TRAIN] + self.improve_steps
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while self.total_steps_counters[RunPhase.TRAIN] < improve_steps_end:
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# TODO if we have an environment, do we want to use it to have the agent train against, and use the
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# collected replay buffer as a dataset? (as oppose to what we currently have, where the dataset is built
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# during heatup, and is composed on random actions)
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# perform several steps of training
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if self.steps_between_evaluation_periods.num_steps > 0:
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with self.phase_context(RunPhase.TRAIN):
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self.reset_internal_state(force_environment_reset=True)
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steps_between_evaluation_periods_end = self.current_step_counter + self.steps_between_evaluation_periods
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while self.current_step_counter < steps_between_evaluation_periods_end:
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self.train()
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# the output of batch RL training is always a checkpoint of the trained agent. we always save a checkpoint,
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# each epoch, regardless of the user's command line arguments.
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self.save_checkpoint()
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# run off-policy evaluation estimators to evaluate the agent's performance against the dataset
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self.run_off_policy_evaluation()
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if self.env_params is not None and self.evaluate(self.evaluation_steps):
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# if we do have a simulator (although we are in a batch RL setting we might have a simulator, e.g. when
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# demonstrating the batch RL use-case using one of the existing Coach environments),
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# we might want to evaluate vs. the simulator every now and then.
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break
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def improve_reward_model(self):
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"""
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:return:
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"""
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screen.log_title("Training a regression model for estimating MDP rewards")
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self.level_managers[0].agents['agent'].improve_reward_model(epochs=self.reward_model_num_epochs)
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def run_off_policy_evaluation(self):
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"""
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Run off-policy evaluation estimators to evaluate the trained policy performance against the dataset
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:return:
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"""
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self.level_managers[0].agents['agent'].run_off_policy_evaluation()
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