'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty)} bottom_critic.embedding_merger_type = EmbeddingMergerType.Concat bottom_critic.middleware_parameters.scheme = [Dense([64])] * 3 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 = Mujoco() env_params.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.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST)] vis_params.dump_mp4 = False 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))
goal_name='state', reward_type=ReachingGoal(distance_from_goal_threshold=0, goal_reaching_reward=0, default_reward=-1), distance_metric=GoalsSpace.DistanceMetric.Euclidean) ############### # Environment # ############### env_params = Mujoco() env_params.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 vis_params = VisualizationParameters() # currently no tests for this preset as the max reward can be accidently achieved. will be fixed with trace based tests. ######## # Test # ######## preset_validation_params = PresetValidationParameters() preset_validation_params.test = True preset_validation_params.min_reward_threshold = -15 preset_validation_params.max_episodes_to_achieve_reward = 10000 graph_manager = BasicRLGraphManager( agent_params=agent_params,
agent_params.input_filter.add_observation_filter('observation', 'clipping', ObservationClippingFilter(-200, 200)) agent_params.pre_network_filter = MujocoInputFilter() 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 = Mujoco() env_params.level = SingleLevelSelection(fetch_v1) env_params.custom_reward_threshold = -49 vis_params = VisualizationParameters() vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()] vis_params.dump_mp4 = False ######## # Test # ######## preset_validation_params = PresetValidationParameters() # preset_validation_params.test = True # preset_validation_params.min_reward_threshold = 200 # preset_validation_params.max_episodes_to_achieve_reward = 600 # preset_validation_params.reward_test_level = 'inverted_pendulum' preset_validation_params.trace_test_levels = ['slide', 'pick_and_place', 'push', 'reach']