from rl_coach.agents.acer_agent import ACERAgentParameters 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 from rl_coach.memories.memory import MemoryGranularity #################### # Graph Scheduling # #################### schedule_params = ScheduleParameters() schedule_params.improve_steps = TrainingSteps(10000000000) schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10) schedule_params.evaluation_steps = EnvironmentEpisodes(1) schedule_params.heatup_steps = EnvironmentSteps(0) ######### # Agent # ######### agent_params = ACERAgentParameters() agent_params.algorithm.num_steps_between_gradient_updates = 5 agent_params.algorithm.ratio_of_replay = 4 agent_params.algorithm.num_transitions_to_start_replay = 1000 agent_params.memory.max_size = (MemoryGranularity.Transitions, 50000) agent_params.input_filter = InputFilter() agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(1/200.)) agent_params.algorithm.beta_entropy = 0.0 ############### # Environment # ############### env_params = GymVectorEnvironment(level='CartPole-v0') ######## # Test # ######## preset_validation_params = PresetValidationParameters() preset_validation_params.test = True preset_validation_params.min_reward_threshold = 150 preset_validation_params.max_episodes_to_achieve_reward = 300 preset_validation_params.num_workers = 1 graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, schedule_params=schedule_params, vis_params=VisualizationParameters(), preset_validation_params=preset_validation_params)