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56 lines
2.4 KiB
Python
56 lines
2.4 KiB
Python
from rl_coach.agents.naf_agent import NAFAgentParameters
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from rl_coach.architectures.tensorflow_components.architecture 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, RunPhase, GradientClippingMethod
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from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod, SingleLevelSelection
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from rl_coach.environments.gym_environment import Mujoco, mujoco_v2
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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(10000000000)
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schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(20)
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schedule_params.evaluation_steps = EnvironmentEpisodes(1)
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schedule_params.heatup_steps = EnvironmentSteps(1000)
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#########
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# Agent #
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#########
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agent_params = NAFAgentParameters()
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agent_params.network_wrappers['main'].input_embedders_parameters['observation'].scheme = [Dense([200])]
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agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense([200])]
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agent_params.network_wrappers['main'].clip_gradients = 1000
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agent_params.network_wrappers['main'].gradients_clipping_method = GradientClippingMethod.ClipByValue
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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 = SingleLevelSelection(mujoco_v2)
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vis_params = VisualizationParameters()
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vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
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vis_params.dump_mp4 = False
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# this preset is currently broken - no test
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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 = 200
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# preset_validation_params.max_episodes_to_achieve_reward = 600
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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=vis_params,
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preset_validation_params=preset_validation_params)
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