Batch RL Tutorial (#372)

rl_coach/presets/Acrobot_DDQN_BCQ_BatchRL.py

@@ -0,0 +1,116 @@
+import tensorflow as tf
+
+from rl_coach.agents.ddqn_agent import DDQNAgentParameters
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
+from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, CsvDataset
+from rl_coach.environments.gym_environment import GymVectorEnvironment
+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 QHeadParameters
+from rl_coach.agents.ddqn_bcq_agent import DDQNBCQAgentParameters
+
+from rl_coach.agents.ddqn_bcq_agent import KNNParameters
+
+DATASET_SIZE = 50000
+
+
+####################
+# 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 #
+#########
+
+agent_params = DDQNBCQAgentParameters()
+agent_params.network_wrappers['main'].batch_size = 128
+# TODO cross-DL framework abstraction for a constant initializer?
+agent_params.network_wrappers['main'].heads_parameters = [QHeadParameters(output_bias_initializer=tf.constant_initializer(-100))]
+
+agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(
+    100)
+agent_params.algorithm.discount = 0.99
+
+agent_params.algorithm.action_drop_method_parameters = KNNParameters()
+
+# 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'].softmax_temperature = 0.2
+
+# ER size
+agent_params.memory = EpisodicExperienceReplayParameters()
+# DATATSET_PATH = 'acrobot.csv'
+# agent_params.memory.load_memory_from_file_path = CsvDataset(DATATSET_PATH, True)
+
+# E-Greedy schedule
+agent_params.exploration.epsilon_schedule = LinearSchedule(0, 0, 10000)
+agent_params.exploration.evaluation_epsilon = 0
+
+# Experience Generating Agent parameters
+experience_generating_agent_params = DDQNAgentParameters()
+
+# schedule parameters
+experience_generating_schedule_params = ScheduleParameters()
+experience_generating_schedule_params.heatup_steps = EnvironmentSteps(1000)
+experience_generating_schedule_params.improve_steps = TrainingSteps(
+    DATASET_SIZE - experience_generating_schedule_params.heatup_steps.num_steps)
+experience_generating_schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10)
+experience_generating_schedule_params.evaluation_steps = EnvironmentEpisodes(1)
+
+# DQN params
+experience_generating_agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(100)
+experience_generating_agent_params.algorithm.discount = 0.99
+experience_generating_agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1)
+
+# NN configuration
+experience_generating_agent_params.network_wrappers['main'].learning_rate = 0.0001
+experience_generating_agent_params.network_wrappers['main'].batch_size = 128
+experience_generating_agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False
+experience_generating_agent_params.network_wrappers['main'].heads_parameters = \
+[QHeadParameters(output_bias_initializer=tf.constant_initializer(-100))]
+
+# ER size
+experience_generating_agent_params.memory = EpisodicExperienceReplayParameters()
+experience_generating_agent_params.memory.max_size = \
+    (MemoryGranularity.Transitions,
+     experience_generating_schedule_params.heatup_steps.num_steps +
+     experience_generating_schedule_params.improve_steps.num_steps + 1)
+
+# E-Greedy schedule
+experience_generating_agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, DATASET_SIZE)
+experience_generating_agent_params.exploration.evaluation_epsilon = 0
+
+
+################
+#  Environment #
+################
+env_params = GymVectorEnvironment(level='Acrobot-v1')
+
+########
+# 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 = 50
+preset_validation_params.read_csv_tries = 500
+
+graph_manager = BatchRLGraphManager(agent_params=agent_params,
+                                    experience_generating_agent_params=experience_generating_agent_params,
+                                    experience_generating_schedule_params=experience_generating_schedule_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=0.4)