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112 lines
4.9 KiB
Python
112 lines
4.9 KiB
Python
from copy import deepcopy
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from rl_coach.architectures.tensorflow_components.layers import Dense
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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
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from rl_coach.environments.gym_environment import GymVectorEnvironment
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from rl_coach.filters.filter import InputFilter
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from rl_coach.filters.reward import RewardRescaleFilter
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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 ClassificationHeadParameters
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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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from rl_coach.agents.ddqn_bcq_agent import NNImitationModelParameters
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DATASET_SIZE = 10000
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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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# using a set of 'unstable' hyper-params to showcase the value of BCQ. Using the same hyper-params with standard DDQN
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# will cause Q values to unboundedly increase, and the policy convergence to be unstable.
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agent_params = DDQNBCQAgentParameters()
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agent_params.network_wrappers['main'].batch_size = 128
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# agent_params.network_wrappers['main'].batch_size = 1024
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# DQN params
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# For making this become Fitted Q-Iteration we can keep the targets constant for the entire dataset size -
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agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(
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DATASET_SIZE / agent_params.network_wrappers['main'].batch_size)
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#
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# agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(
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# 3)
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agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(0)
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agent_params.algorithm.discount = 0.98
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# can use either a kNN or a NN based model for predicting which actions not to max over in the bellman equation
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agent_params.algorithm.action_drop_method_parameters = KNNParameters()
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# agent_params.algorithm.action_drop_method_parameters = NNImitationModelParameters()
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# agent_params.algorithm.action_drop_method_parameters.imitation_model_num_epochs = 500
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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'].l2_regularization = 0.0001
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agent_params.network_wrappers['main'].softmax_temperature = 0.2
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# reward model params
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agent_params.network_wrappers['reward_model'] = deepcopy(agent_params.network_wrappers['main'])
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agent_params.network_wrappers['reward_model'].learning_rate = 0.0001
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agent_params.network_wrappers['reward_model'].l2_regularization = 0
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agent_params.network_wrappers['imitation_model'] = deepcopy(agent_params.network_wrappers['main'])
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agent_params.network_wrappers['imitation_model'].learning_rate = 0.0001
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agent_params.network_wrappers['imitation_model'].l2_regularization = 0
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agent_params.network_wrappers['imitation_model'].heads_parameters = [ClassificationHeadParameters()]
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agent_params.network_wrappers['imitation_model'].input_embedders_parameters['observation'].scheme = \
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[Dense(1024), Dense(1024), Dense(512), Dense(512), Dense(256)]
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agent_params.network_wrappers['imitation_model'].middleware_parameters.scheme = [Dense(128), Dense(64)]
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# ER size
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agent_params.memory = EpisodicExperienceReplayParameters()
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agent_params.memory.max_size = (MemoryGranularity.Transitions, DATASET_SIZE)
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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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agent_params.input_filter = InputFilter()
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agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(1/200.))
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################
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# Environment #
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################
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env_params = GymVectorEnvironment(level='CartPole-v0')
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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 = 2000
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graph_manager = BatchRLGraphManager(agent_params=agent_params, 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=0.8)
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