40) agent_params.algorithm.rate_for_copying_weights_to_target = 0.05 agent_params.memory.max_size = (MemoryGranularity.Transitions, 10**6) agent_params.exploration.epsilon_schedule = ConstantSchedule(0.2) agent_params.exploration.evaluation_epsilon = 0 ############### # Environment # ############### env_params = GymVectorEnvironment( level='rl_coach.environments.toy_problems.bit_flip:BitFlip') env_params.additional_simulator_parameters = { 'bit_length': bit_length, 'mean_zero': True } env_params.custom_reward_threshold = -bit_length + 1 ######## # Test # ######## preset_validation_params = PresetValidationParameters() preset_validation_params.test = True preset_validation_params.min_reward_threshold = -7.9 preset_validation_params.max_episodes_to_achieve_reward = 10000 graph_manager = BasicRLGraphManager( agent_params=agent_params, env_params=env_params, schedule_params=schedule_params, vis_params=VisualizationParameters(), preset_validation_params=preset_validation_params)
agent_params.exploration.continuous_exploration_policy_parameters.evaluation_noise = 0 agent_params.input_filter = InputFilter() agent_params.input_filter.add_observation_filter('observation', 'clipping', ObservationClippingFilter(-200, 200)) agent_params.pre_network_filter = InputFilter() agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation', ObservationNormalizationFilter(name='normalize_observation')) agent_params.pre_network_filter.add_observation_filter('achieved_goal', 'normalize_achieved_goal', ObservationNormalizationFilter(name='normalize_achieved_goal')) agent_params.pre_network_filter.add_observation_filter('desired_goal', 'normalize_desired_goal', ObservationNormalizationFilter(name='normalize_desired_goal')) ############### # Environment # ############### env_params = GymVectorEnvironment(level=SingleLevelSelection(fetch_v1)) env_params.custom_reward_threshold = -49 ######## # Test # ######## preset_validation_params = PresetValidationParameters() preset_validation_params.trace_test_levels = ['slide', 'pick_and_place', 'push', 'reach'] graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, schedule_params=schedule_params, vis_params=VisualizationParameters(), preset_validation_params=preset_validation_params)
bottom_critic.learning_rate = 0.001 bottom_critic.batch_size = 4096 agents_params = [top_agent_params, bottom_agent_params] ############### # Environment # ############### time_limit = 1000 env_params = GymVectorEnvironment( level="rl_coach.environments.mujoco.pendulum_with_goals:PendulumWithGoals") env_params.additional_simulator_parameters = { "time_limit": time_limit, "random_goals_instead_of_standing_goal": False, "polar_coordinates": polar_coordinates, "goal_reaching_thresholds": distance_from_goal_threshold } env_params.frame_skip = 10 env_params.custom_reward_threshold = -time_limit + 1 vis_params = VisualizationParameters() vis_params.native_rendering = False graph_manager = HACGraphManager( agents_params=agents_params, env_params=env_params, schedule_params=schedule_params, vis_params=vis_params, consecutive_steps_to_run_non_top_levels=EnvironmentSteps(40))