BCQ variant on top of DDQN (#276)

* kNN based model for predicting which actions to drop
* fix for seeds with batch rl

rl_coach/presets/CartPole_DQN_BatchRL_BCQ.py

@@ -0,0 +1,111 @@
+from copy import deepcopy
+
+from rl_coach.architectures.tensorflow_components.layers import Dense
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
+from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
+from rl_coach.environments.gym_environment import GymVectorEnvironment
+from rl_coach.filters.filter import InputFilter
+from rl_coach.filters.reward import RewardRescaleFilter
+from rl_coach.graph_managers.batch_rl_graph_manager import BatchRLGraphManager
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+from rl_coach.memories.memory import MemoryGranularity
+from rl_coach.schedules import LinearSchedule
+from rl_coach.memories.episodic import EpisodicExperienceReplayParameters
+from rl_coach.architectures.head_parameters import ClassificationHeadParameters
+from rl_coach.agents.ddqn_bcq_agent import DDQNBCQAgentParameters
+
+from rl_coach.agents.ddqn_bcq_agent import KNNParameters
+from rl_coach.agents.ddqn_bcq_agent import NNImitationModelParameters
+
+DATASET_SIZE = 10000
+
+####################
+# Graph Scheduling #
+####################
+
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = TrainingSteps(10000000000)
+schedule_params.steps_between_evaluation_periods = TrainingSteps(1)
+schedule_params.evaluation_steps = EnvironmentEpisodes(10)
+schedule_params.heatup_steps = EnvironmentSteps(DATASET_SIZE)
+
+#########
+# Agent #
+#########
+
+# using a set of 'unstable' hyper-params to showcase the value of BCQ. Using the same hyper-params with standard DDQN
+# will cause Q values to unboundedly increase, and the policy convergence to be unstable.
+agent_params = DDQNBCQAgentParameters()
+agent_params.network_wrappers['main'].batch_size = 128
+# agent_params.network_wrappers['main'].batch_size = 1024
+
+# DQN params
+
+# For making this become Fitted Q-Iteration we can keep the targets constant for the entire dataset size -
+agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(
+    DATASET_SIZE / agent_params.network_wrappers['main'].batch_size)
+#
+# agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(
+#     3)
+
+agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(0)
+agent_params.algorithm.discount = 0.98
+
+# can use either a kNN or a NN based model for predicting which actions not to max over in the bellman equation
+agent_params.algorithm.action_drop_method_parameters = KNNParameters()
+# agent_params.algorithm.action_drop_method_parameters = NNImitationModelParameters()
+# agent_params.algorithm.action_drop_method_parameters.imitation_model_num_epochs = 500
+
+# NN configuration
+agent_params.network_wrappers['main'].learning_rate = 0.0001
+agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False
+agent_params.network_wrappers['main'].l2_regularization = 0.0001
+agent_params.network_wrappers['main'].softmax_temperature = 0.2
+
+# reward model params
+agent_params.network_wrappers['reward_model'] = deepcopy(agent_params.network_wrappers['main'])
+agent_params.network_wrappers['reward_model'].learning_rate = 0.0001
+agent_params.network_wrappers['reward_model'].l2_regularization = 0
+
+agent_params.network_wrappers['imitation_model'] = deepcopy(agent_params.network_wrappers['main'])
+agent_params.network_wrappers['imitation_model'].learning_rate = 0.0001
+agent_params.network_wrappers['imitation_model'].l2_regularization = 0
+
+agent_params.network_wrappers['imitation_model'].heads_parameters = [ClassificationHeadParameters()]
+agent_params.network_wrappers['imitation_model'].input_embedders_parameters['observation'].scheme = \
+    [Dense(1024), Dense(1024), Dense(512), Dense(512), Dense(256)]
+agent_params.network_wrappers['imitation_model'].middleware_parameters.scheme = [Dense(128), Dense(64)]
+
+
+# ER size
+agent_params.memory = EpisodicExperienceReplayParameters()
+agent_params.memory.max_size = (MemoryGranularity.Transitions, DATASET_SIZE)
+
+
+# E-Greedy schedule
+agent_params.exploration.epsilon_schedule = LinearSchedule(0, 0, 10000)
+agent_params.exploration.evaluation_epsilon = 0
+
+
+agent_params.input_filter = InputFilter()
+agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(1/200.))
+
+################
+#  Environment #
+################
+env_params = GymVectorEnvironment(level='CartPole-v0')
+
+########
+# Test #
+########
+preset_validation_params = PresetValidationParameters()
+preset_validation_params.test = True
+preset_validation_params.min_reward_threshold = 150
+preset_validation_params.max_episodes_to_achieve_reward = 2000
+
+graph_manager = BatchRLGraphManager(agent_params=agent_params, env_params=env_params,
+                                    schedule_params=schedule_params,
+                                    vis_params=VisualizationParameters(dump_signals_to_csv_every_x_episodes=1),
+                                    preset_validation_params=preset_validation_params,
+                                    reward_model_num_epochs=30,
+                                    train_to_eval_ratio=0.8)