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117 lines
5.1 KiB
Python
117 lines
5.1 KiB
Python
import tensorflow as tf
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from rl_coach.agents.ddqn_agent import DDQNAgentParameters
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from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
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from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, CsvDataset
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from rl_coach.environments.gym_environment import GymVectorEnvironment
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from rl_coach.graph_managers.batch_rl_graph_manager import BatchRLGraphManager
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from rl_coach.graph_managers.graph_manager import ScheduleParameters
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from rl_coach.memories.memory import MemoryGranularity
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from rl_coach.schedules import LinearSchedule
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from rl_coach.memories.episodic import EpisodicExperienceReplayParameters
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from rl_coach.architectures.head_parameters import QHeadParameters
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from rl_coach.agents.ddqn_bcq_agent import DDQNBCQAgentParameters
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from rl_coach.agents.ddqn_bcq_agent import KNNParameters
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DATASET_SIZE = 50000
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####################
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# Graph Scheduling #
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####################
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schedule_params = ScheduleParameters()
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schedule_params.improve_steps = TrainingSteps(10000000000)
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schedule_params.steps_between_evaluation_periods = TrainingSteps(1)
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schedule_params.evaluation_steps = EnvironmentEpisodes(10)
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schedule_params.heatup_steps = EnvironmentSteps(DATASET_SIZE)
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#########
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# Agent #
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#########
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agent_params = DDQNBCQAgentParameters()
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agent_params.network_wrappers['main'].batch_size = 128
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# TODO cross-DL framework abstraction for a constant initializer?
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agent_params.network_wrappers['main'].heads_parameters = [QHeadParameters(output_bias_initializer=tf.constant_initializer(-100))]
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agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(
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100)
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agent_params.algorithm.discount = 0.99
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agent_params.algorithm.action_drop_method_parameters = KNNParameters()
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# NN configuration
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agent_params.network_wrappers['main'].learning_rate = 0.0001
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agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False
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agent_params.network_wrappers['main'].softmax_temperature = 0.2
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# ER size
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agent_params.memory = EpisodicExperienceReplayParameters()
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# DATATSET_PATH = 'acrobot.csv'
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# agent_params.memory.load_memory_from_file_path = CsvDataset(DATATSET_PATH, True)
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# E-Greedy schedule
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agent_params.exploration.epsilon_schedule = LinearSchedule(0, 0, 10000)
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agent_params.exploration.evaluation_epsilon = 0
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# Experience Generating Agent parameters
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experience_generating_agent_params = DDQNAgentParameters()
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# schedule parameters
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experience_generating_schedule_params = ScheduleParameters()
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experience_generating_schedule_params.heatup_steps = EnvironmentSteps(1000)
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experience_generating_schedule_params.improve_steps = TrainingSteps(
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DATASET_SIZE - experience_generating_schedule_params.heatup_steps.num_steps)
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experience_generating_schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10)
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experience_generating_schedule_params.evaluation_steps = EnvironmentEpisodes(1)
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# DQN params
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experience_generating_agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(100)
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experience_generating_agent_params.algorithm.discount = 0.99
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experience_generating_agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1)
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# NN configuration
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experience_generating_agent_params.network_wrappers['main'].learning_rate = 0.0001
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experience_generating_agent_params.network_wrappers['main'].batch_size = 128
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experience_generating_agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False
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experience_generating_agent_params.network_wrappers['main'].heads_parameters = \
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[QHeadParameters(output_bias_initializer=tf.constant_initializer(-100))]
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# ER size
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experience_generating_agent_params.memory = EpisodicExperienceReplayParameters()
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experience_generating_agent_params.memory.max_size = \
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(MemoryGranularity.Transitions,
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experience_generating_schedule_params.heatup_steps.num_steps +
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experience_generating_schedule_params.improve_steps.num_steps + 1)
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# E-Greedy schedule
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experience_generating_agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, DATASET_SIZE)
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experience_generating_agent_params.exploration.evaluation_epsilon = 0
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################
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# Environment #
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################
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env_params = GymVectorEnvironment(level='Acrobot-v1')
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########
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# Test #
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########
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preset_validation_params = PresetValidationParameters()
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preset_validation_params.test = True
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preset_validation_params.min_reward_threshold = 150
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preset_validation_params.max_episodes_to_achieve_reward = 50
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preset_validation_params.read_csv_tries = 500
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graph_manager = BatchRLGraphManager(agent_params=agent_params,
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experience_generating_agent_params=experience_generating_agent_params,
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experience_generating_schedule_params=experience_generating_schedule_params,
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env_params=env_params,
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schedule_params=schedule_params,
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vis_params=VisualizationParameters(dump_signals_to_csv_every_x_episodes=1),
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preset_validation_params=preset_validation_params,
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reward_model_num_epochs=30,
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train_to_eval_ratio=0.4)
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