from rl_coach.agents.policy_gradients_agent import PolicyGradientsAgentParameters from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.gym_environment import GymVectorEnvironment from rl_coach.filters.filter import InputFilter from rl_coach.filters.reward.reward_rescale_filter import RewardRescaleFilter from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager from rl_coach.graph_managers.graph_manager import ScheduleParameters #################### # Graph Scheduling # #################### schedule_params = ScheduleParameters() schedule_params.improve_steps = TrainingSteps(10000000000) schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(20) schedule_params.evaluation_steps = EnvironmentEpisodes(1) schedule_params.heatup_steps = EnvironmentSteps(0) ######### # Agent # ######### agent_params = PolicyGradientsAgentParameters() agent_params.algorithm.discount = 0.99 agent_params.algorithm.apply_gradients_every_x_episodes = 5 agent_params.algorithm.num_steps_between_gradient_updates = 20000 agent_params.network_wrappers['main'].optimizer_type = 'Adam' agent_params.network_wrappers['main'].learning_rate = 0.0005 agent_params.input_filter = InputFilter() agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(1/200.)) ############### # Environment # ############### env_params = GymVectorEnvironment(level='CartPole-v0') ######## # Test # ######## preset_validation_params = PresetValidationParameters() preset_validation_params.test = True preset_validation_params.min_reward_threshold = 130 preset_validation_params.max_episodes_to_achieve_reward = 550 graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, schedule_params=schedule_params, vis_params=VisualizationParameters(), preset_validation_params=preset_validation_params)