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42 lines
1.8 KiB
Python
42 lines
1.8 KiB
Python
from rl_coach.agents.policy_gradients_agent import PolicyGradientsAgentParameters
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from rl_coach.base_parameters import VisualizationParameters
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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.observation.observation_normalization_filter import ObservationNormalizationFilter
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from rl_coach.filters.reward.reward_rescale_filter import RewardRescaleFilter
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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(50)
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schedule_params.evaluation_steps = EnvironmentEpisodes(3)
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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 = PolicyGradientsAgentParameters()
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agent_params.algorithm.apply_gradients_every_x_episodes = 5
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agent_params.algorithm.num_steps_between_gradient_updates = 20000
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agent_params.network_wrappers['main'].learning_rate = 0.0005
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agent_params.input_filter = InputFilter()
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agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(1/20.))
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agent_params.input_filter.add_observation_filter('observation', 'normalize', ObservationNormalizationFilter())
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###############
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# Environment #
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###############
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env_params = GymVectorEnvironment(level="InvertedPendulum-v2")
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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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