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7b0fccb041
* GraphManager.set_session also sets self.sess * make sure that GraphManager.fetch_from_worker uses training phase * remove unnecessary phase setting in training worker * reorganize rollout worker * provide default name to GlobalVariableSaver.__init__ since it isn't really used anyway * allow dividing TrainingSteps and EnvironmentSteps * add timestamps to the log * added redis data store * conflict merge fix
61 lines
2.8 KiB
Python
61 lines
2.8 KiB
Python
from rl_coach.agents.ppo_agent import PPOAgentParameters
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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.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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####################
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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(1e10)
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schedule_params.steps_between_evaluation_periods = EnvironmentSteps(4000)
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schedule_params.evaluation_steps = EnvironmentEpisodes(1)
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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 = PPOAgentParameters()
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agent_params.network_wrappers['actor'].learning_rate = 5e-5
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agent_params.network_wrappers['critic'].learning_rate = 5e-5
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agent_params.network_wrappers['actor'].input_embedders_parameters['observation'].scheme = [Dense(64)]
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agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense(64)]
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agent_params.network_wrappers['critic'].input_embedders_parameters['observation'].scheme = [Dense(64)]
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agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(64)]
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agent_params.input_filter = InputFilter()
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agent_params.input_filter.add_observation_filter('observation', 'normalize', ObservationNormalizationFilter())
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agent_params.algorithm.initial_kl_coefficient = 0.2
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agent_params.algorithm.gae_lambda = 1.0
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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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###############
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# Environment #
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###############
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env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2))
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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 = 3000
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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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