'main'].learning_rate = 0.00005 # called alpha in the paper agent_params.algorithm.huber_loss_interval = 1 # k = 0 for strict quantile loss, k = 1 for Huber quantile loss ############### # Environment # ############### env_params = Atari() env_params.level = SingleLevelSelection(atari_deterministic_v4) 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.trace_test_levels = [ 'breakout', 'pong', 'space_invaders' ] graph_manager = BasicRLGraphManager( agent_params=agent_params, env_params=env_params, schedule_params=schedule_params, vis_params=vis_params, preset_validation_params=preset_validation_params)
agent_params.network_wrappers['actor'].input_embedders_parameters.pop('observation') agent_params.network_wrappers['critic'].input_embedders_parameters['measurements'] = \ agent_params.network_wrappers['critic'].input_embedders_parameters.pop('observation') agent_params.network_wrappers['actor'].input_embedders_parameters['measurements'].scheme = [Dense([300])] agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense([200])] agent_params.network_wrappers['critic'].input_embedders_parameters['measurements'].scheme = [Dense([400])] agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense([300])] agent_params.network_wrappers['critic'].input_embedders_parameters['action'].scheme = EmbedderScheme.Empty agent_params.input_filter = MujocoInputFilter() agent_params.input_filter.add_reward_filter("rescale", RewardRescaleFilter(1/10.)) ############### # Environment # ############### env_params = ControlSuiteEnvironmentParameters() env_params.level = SingleLevelSelection(control_suite_envs) 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.trace_test_levels = ['cartpole:swingup', 'hopper:hop'] graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, schedule_params=schedule_params, vis_params=vis_params, preset_validation_params=preset_validation_params)
agent_params.network_wrappers['main'].input_embedders_parameters['observation'] = \ InputEmbedderParameters(scheme=[Dense(200)]) agent_params.network_wrappers['main'].middleware_parameters = LSTMMiddlewareParameters(scheme=MiddlewareScheme.Empty, number_of_lstm_cells=128) agent_params.input_filter = InputFilter() agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(1/20.)) agent_params.input_filter.add_observation_filter('observation', 'normalize', ObservationNormalizationFilter()) ############### # Environment # ############### env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2)) ######## # Test # ######## preset_validation_params = PresetValidationParameters() preset_validation_params.test = False preset_validation_params.min_reward_threshold = 400 preset_validation_params.max_episodes_to_achieve_reward = 1000 preset_validation_params.num_workers = 8 preset_validation_params.reward_test_level = 'inverted_pendulum' preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper'] 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)
'main'].middleware_parameters = FCMiddlewareParameters() agent_params.network_wrappers['main'].learning_rate = 0.0001 agent_params.exploration = CategoricalParameters() ############### # Environment # ############### env_params = Atari() env_params.level = SingleLevelSelection(atari_deterministic_v4) 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.trace_test_levels = ['breakout', 'pong', 'alien'] graph_manager = BasicRLGraphManager( agent_params=agent_params, env_params=env_params, schedule_params=schedule_params, vis_params=vis_params, preset_validation_params=preset_validation_params)