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77 lines
3.6 KiB
Python
77 lines
3.6 KiB
Python
from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters
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from rl_coach.architectures.layers import Dense
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from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType
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from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
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from rl_coach.environments.environment import SingleLevelSelection
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from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
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from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
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from rl_coach.filters.filter import InputFilter
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from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter
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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.schedules import LinearSchedule
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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(10000000)
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schedule_params.steps_between_evaluation_periods = EnvironmentSteps(2048)
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schedule_params.evaluation_steps = EnvironmentEpisodes(5)
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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 = ClippedPPOAgentParameters()
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agent_params.network_wrappers['main'].learning_rate = 0.0003
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agent_params.network_wrappers['main'].input_embedders_parameters['observation'].activation_function = 'tanh'
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agent_params.network_wrappers['main'].input_embedders_parameters['observation'].scheme = [Dense(64)]
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agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense(64)]
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agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'tanh'
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agent_params.network_wrappers['main'].batch_size = 64
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agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5
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agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999
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agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
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agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
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agent_params.algorithm.beta_entropy = 0
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agent_params.algorithm.gae_lambda = 0.95
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agent_params.algorithm.discount = 0.99
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agent_params.algorithm.optimization_epochs = 10
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agent_params.algorithm.estimate_state_value_using_gae = True
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# Distributed Coach synchronization type.
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agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC
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agent_params.input_filter = InputFilter()
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agent_params.exploration = AdditiveNoiseParameters()
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agent_params.pre_network_filter = InputFilter()
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agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation',
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ObservationNormalizationFilter(name='normalize_observation'))
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###############
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# Environment #
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###############
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env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2))
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# Set the target success
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env_params.target_success_rate = 1.0
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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 = 400
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preset_validation_params.max_episodes_to_achieve_reward = 1000
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preset_validation_params.reward_test_level = 'inverted_pendulum'
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preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper']
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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=VisualizationParameters(),
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preset_validation_params=preset_validation_params)
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