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82 lines
3.9 KiB
Python
82 lines
3.9 KiB
Python
from rl_coach.architectures.tensorflow_components.architecture import Dense
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from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, InputEmbedderParameters, \
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PresetValidationParameters
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from rl_coach.environments.gym_environment import Mujoco
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from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
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from rl_coach.graph_managers.graph_manager import ScheduleParameters
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from rl_coach.memories.episodic.episodic_hindsight_experience_replay import \
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EpisodicHindsightExperienceReplayParameters, HindsightGoalSelectionMethod
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from rl_coach.memories.memory import MemoryGranularity
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from rl_coach.schedules import ConstantSchedule
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from rl_coach.spaces import GoalsSpace, ReachingGoal
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from rl_coach.agents.dqn_agent import DQNAgentParameters
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from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
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bit_length = 20
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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 = EnvironmentEpisodes(16 * 50 * 200) # 200 epochs
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schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(16 * 50) # 50 cycles
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schedule_params.evaluation_steps = EnvironmentEpisodes(10)
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schedule_params.heatup_steps = EnvironmentSteps(0)
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#########
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# Agent #
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#########
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agent_params = DQNAgentParameters()
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agent_params.network_wrappers['main'].learning_rate = 0.001
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agent_params.network_wrappers['main'].batch_size = 128
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agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense([256])]
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agent_params.network_wrappers['main'].input_embedders_parameters = {
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'state': InputEmbedderParameters(scheme=EmbedderScheme.Empty),
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'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty)}
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agent_params.algorithm.discount = 0.98
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agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(16)
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agent_params.algorithm.num_consecutive_training_steps = 40
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agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(40)
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agent_params.algorithm.rate_for_copying_weights_to_target = 0.05
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agent_params.memory.max_size = (MemoryGranularity.Transitions, 10**6)
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agent_params.exploration.epsilon_schedule = ConstantSchedule(0.2)
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agent_params.exploration.evaluation_epsilon = 0
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agent_params.memory = EpisodicHindsightExperienceReplayParameters()
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agent_params.memory.hindsight_goal_selection_method = HindsightGoalSelectionMethod.Final
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agent_params.memory.hindsight_transitions_per_regular_transition = 1
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agent_params.memory.goals_space = GoalsSpace(goal_name='state',
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reward_type=ReachingGoal(distance_from_goal_threshold=0,
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goal_reaching_reward=0,
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default_reward=-1),
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distance_metric=GoalsSpace.DistanceMetric.Euclidean)
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###############
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# Environment #
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###############
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env_params = Mujoco()
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env_params.level = 'rl_coach.environments.toy_problems.bit_flip:BitFlip'
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env_params.additional_simulator_parameters = {'bit_length': bit_length, 'mean_zero': True}
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env_params.custom_reward_threshold = -bit_length + 1
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vis_params = VisualizationParameters()
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# currently no tests for this preset as the max reward can be accidently achieved. will be fixed with trace based tests.
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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 = -15
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preset_validation_params.max_episodes_to_achieve_reward = 10000
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graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
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schedule_params=schedule_params, vis_params=vis_params,
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preset_validation_params=preset_validation_params)
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# self.algorithm.add_intrinsic_reward_for_reaching_the_goal = False
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