def _setup_simtrace_mp4_writers(self): """Setup the simtrace and mp4 writers if the locations are passed in. """ self._is_save_simtrace_enabled = False self._is_save_mp4_enabled = False if hasattr(self._current_racer, 'outputSimTrace'): self._simtrace_video_s3_writers.append( SimtraceVideo(upload_type=SimtraceVideoNames.SIMTRACE_EVAL.value, bucket=self._current_racer.outputSimTrace.s3BucketName, s3_prefix=self._current_racer.outputSimTrace.s3KeyPrefix, region_name=self._region, local_path=SIMTRACE_EVAL_LOCAL_PATH_FORMAT.format(self._agent_name))) self._is_save_simtrace_enabled = True if hasattr(self._current_racer, 'outputMp4'): self._simtrace_video_s3_writers.extend([ SimtraceVideo(upload_type=SimtraceVideoNames.PIP.value, bucket=self._current_racer.outputMp4.s3BucketName, s3_prefix=self._current_racer.outputMp4.s3KeyPrefix, region_name=self._region, local_path=CAMERA_PIP_MP4_LOCAL_PATH_FORMAT.format(self._agent_name)), SimtraceVideo(upload_type=SimtraceVideoNames.DEGREE45.value, bucket=self._current_racer.outputMp4.s3BucketName, s3_prefix=self._current_racer.outputMp4.s3KeyPrefix, region_name=self._region, local_path=CAMERA_45DEGREE_LOCAL_PATH_FORMAT.format(self._agent_name)), SimtraceVideo(upload_type=SimtraceVideoNames.TOPVIEW.value, bucket=self._current_racer.outputMp4.s3BucketName, s3_prefix=self._current_racer.outputMp4.s3KeyPrefix, region_name=self._region, local_path=CAMERA_TOPVIEW_LOCAL_PATH_FORMAT.format(self._agent_name))]) self._is_save_mp4_enabled = True
def main(): screen.set_use_colors(False) parser = argparse.ArgumentParser() parser.add_argument( '-c', '--checkpoint_dir', help= '(string) Path to a folder containing a checkpoint to restore the model from.', type=str, default='./checkpoint') parser.add_argument('--s3_bucket', help='(string) S3 bucket', type=str, default=rospy.get_param("SAGEMAKER_SHARED_S3_BUCKET", "gsaur-test")) parser.add_argument('--s3_prefix', help='(string) S3 prefix', type=str, default=rospy.get_param("SAGEMAKER_SHARED_S3_PREFIX", "sagemaker")) parser.add_argument( '--num_workers', help="(int) The number of workers started in this pool", type=int, default=int(rospy.get_param("NUM_WORKERS", 1))) parser.add_argument('--rollout_idx', help="(int) The index of current rollout worker", type=int, default=0) parser.add_argument('-r', '--redis_ip', help="(string) IP or host for the redis server", default='localhost', type=str) parser.add_argument('-rp', '--redis_port', help="(int) Port of the redis server", default=6379, type=int) parser.add_argument('--aws_region', help='(string) AWS region', type=str, default=rospy.get_param("AWS_REGION", "us-east-1")) parser.add_argument('--reward_file_s3_key', help='(string) Reward File S3 Key', type=str, default=rospy.get_param("REWARD_FILE_S3_KEY", None)) parser.add_argument('--model_metadata_s3_key', help='(string) Model Metadata File S3 Key', type=str, default=rospy.get_param("MODEL_METADATA_FILE_S3_KEY", None)) # For training job, reset is not allowed. penalty_seconds, off_track_penalty, and # collision_penalty will all be 0 be default parser.add_argument('--number_of_resets', help='(integer) Number of resets', type=int, default=int(rospy.get_param("NUMBER_OF_RESETS", 0))) parser.add_argument('--penalty_seconds', help='(float) penalty second', type=float, default=float(rospy.get_param("PENALTY_SECONDS", 0.0))) parser.add_argument('--job_type', help='(string) job type', type=str, default=rospy.get_param("JOB_TYPE", "TRAINING")) parser.add_argument('--is_continuous', help='(boolean) is continous after lap completion', type=bool, default=utils.str2bool( rospy.get_param("IS_CONTINUOUS", False))) parser.add_argument('--race_type', help='(string) Race type', type=str, default=rospy.get_param("RACE_TYPE", "TIME_TRIAL")) parser.add_argument('--off_track_penalty', help='(float) off track penalty second', type=float, default=float(rospy.get_param("OFF_TRACK_PENALTY", 0.0))) parser.add_argument('--collision_penalty', help='(float) collision penalty second', type=float, default=float(rospy.get_param("COLLISION_PENALTY", 0.0))) args = parser.parse_args() logger.info("S3 bucket: %s", args.s3_bucket) logger.info("S3 prefix: %s", args.s3_prefix) # Download and import reward function # TODO: replace 'agent' with name of each agent for multi-agent training reward_function_file = RewardFunction( bucket=args.s3_bucket, s3_key=args.reward_file_s3_key, region_name=args.aws_region, local_path=REWARD_FUCTION_LOCAL_PATH_FORMAT.format('agent')) reward_function = reward_function_file.get_reward_function() # Instantiate Cameras configure_camera(namespaces=['racecar']) preset_file_success, _ = download_custom_files_if_present( s3_bucket=args.s3_bucket, s3_prefix=args.s3_prefix, aws_region=args.aws_region) # download model metadata # TODO: replace 'agent' with name of each agent model_metadata = ModelMetadata( bucket=args.s3_bucket, s3_key=args.model_metadata_s3_key, region_name=args.aws_region, local_path=MODEL_METADATA_LOCAL_PATH_FORMAT.format('agent')) model_metadata_info = model_metadata.get_model_metadata_info() version = model_metadata_info[ModelMetadataKeys.VERSION.value] agent_config = { 'model_metadata': model_metadata, ConfigParams.CAR_CTRL_CONFIG.value: { ConfigParams.LINK_NAME_LIST.value: LINK_NAMES, ConfigParams.VELOCITY_LIST.value: VELOCITY_TOPICS, ConfigParams.STEERING_LIST.value: STEERING_TOPICS, ConfigParams.CHANGE_START.value: utils.str2bool(rospy.get_param('CHANGE_START_POSITION', True)), ConfigParams.ALT_DIR.value: utils.str2bool( rospy.get_param('ALTERNATE_DRIVING_DIRECTION', False)), ConfigParams.MODEL_METADATA.value: model_metadata, ConfigParams.REWARD.value: reward_function, ConfigParams.AGENT_NAME.value: 'racecar', ConfigParams.VERSION.value: version, ConfigParams.NUMBER_OF_RESETS.value: args.number_of_resets, ConfigParams.PENALTY_SECONDS.value: args.penalty_seconds, ConfigParams.NUMBER_OF_TRIALS.value: None, ConfigParams.IS_CONTINUOUS.value: args.is_continuous, ConfigParams.RACE_TYPE.value: args.race_type, ConfigParams.COLLISION_PENALTY.value: args.collision_penalty, ConfigParams.OFF_TRACK_PENALTY.value: args.off_track_penalty } } #! TODO each agent should have own s3 bucket metrics_key = rospy.get_param('METRICS_S3_OBJECT_KEY') if args.num_workers > 1 and args.rollout_idx > 0: key_tuple = os.path.splitext(metrics_key) metrics_key = "{}_{}{}".format(key_tuple[0], str(args.rollout_idx), key_tuple[1]) metrics_s3_config = { MetricsS3Keys.METRICS_BUCKET.value: rospy.get_param('METRICS_S3_BUCKET'), MetricsS3Keys.METRICS_KEY.value: metrics_key, MetricsS3Keys.REGION.value: rospy.get_param('AWS_REGION') } run_phase_subject = RunPhaseSubject() agent_list = list() #TODO: replace agent for multi agent training # checkpoint s3 instance # TODO replace agent with agent_0 and so on for multiagent case checkpoint = Checkpoint(bucket=args.s3_bucket, s3_prefix=args.s3_prefix, region_name=args.aws_region, agent_name='agent', checkpoint_dir=args.checkpoint_dir) agent_list.append( create_rollout_agent( agent_config, TrainingMetrics( agent_name='agent', s3_dict_metrics=metrics_s3_config, deepracer_checkpoint_json=checkpoint.deepracer_checkpoint_json, ckpnt_dir=os.path.join(args.checkpoint_dir, 'agent'), run_phase_sink=run_phase_subject, use_model_picker=(args.rollout_idx == 0)), run_phase_subject)) agent_list.append(create_obstacles_agent()) agent_list.append(create_bot_cars_agent()) # ROS service to indicate all the robomaker markov packages are ready for consumption signal_robomaker_markov_package_ready() PhaseObserver('/agent/training_phase', run_phase_subject) aws_region = rospy.get_param('AWS_REGION', args.aws_region) simtrace_s3_bucket = rospy.get_param('SIMTRACE_S3_BUCKET', None) mp4_s3_bucket = rospy.get_param('MP4_S3_BUCKET', None) if args.rollout_idx == 0 else None if simtrace_s3_bucket: simtrace_s3_object_prefix = rospy.get_param('SIMTRACE_S3_PREFIX') if args.num_workers > 1: simtrace_s3_object_prefix = os.path.join(simtrace_s3_object_prefix, str(args.rollout_idx)) if mp4_s3_bucket: mp4_s3_object_prefix = rospy.get_param('MP4_S3_OBJECT_PREFIX') simtrace_video_s3_writers = [] #TODO: replace 'agent' with 'agent_0' for multi agent training and # mp4_s3_object_prefix, mp4_s3_bucket will be a list, so need to access with index if simtrace_s3_bucket: simtrace_video_s3_writers.append( SimtraceVideo( upload_type=SimtraceVideoNames.SIMTRACE_TRAINING.value, bucket=simtrace_s3_bucket, s3_prefix=simtrace_s3_object_prefix, region_name=aws_region, local_path=SIMTRACE_TRAINING_LOCAL_PATH_FORMAT.format( 'agent'))) if mp4_s3_bucket: simtrace_video_s3_writers.extend([ SimtraceVideo( upload_type=SimtraceVideoNames.PIP.value, bucket=mp4_s3_bucket, s3_prefix=mp4_s3_object_prefix, region_name=aws_region, local_path=CAMERA_PIP_MP4_LOCAL_PATH_FORMAT.format('agent')), SimtraceVideo( upload_type=SimtraceVideoNames.DEGREE45.value, bucket=mp4_s3_bucket, s3_prefix=mp4_s3_object_prefix, region_name=aws_region, local_path=CAMERA_45DEGREE_LOCAL_PATH_FORMAT.format('agent')), SimtraceVideo( upload_type=SimtraceVideoNames.TOPVIEW.value, bucket=mp4_s3_bucket, s3_prefix=mp4_s3_object_prefix, region_name=aws_region, local_path=CAMERA_TOPVIEW_LOCAL_PATH_FORMAT.format('agent')) ]) # TODO: replace 'agent' with specific agent name for multi agent training ip_config = IpConfig(bucket=args.s3_bucket, s3_prefix=args.s3_prefix, region_name=args.aws_region, local_path=IP_ADDRESS_LOCAL_PATH.format('agent')) redis_ip = ip_config.get_ip_config() # Download hyperparameters from SageMaker shared s3 bucket # TODO: replace 'agent' with name of each agent hyperparameters = Hyperparameters( bucket=args.s3_bucket, s3_key=get_s3_key(args.s3_prefix, HYPERPARAMETER_S3_POSTFIX), region_name=args.aws_region, local_path=HYPERPARAMETER_LOCAL_PATH_FORMAT.format('agent')) sm_hyperparams_dict = hyperparameters.get_hyperparameters_dict() enable_domain_randomization = utils.str2bool( rospy.get_param('ENABLE_DOMAIN_RANDOMIZATION', False)) # Make the clients that will allow us to pause and unpause the physics rospy.wait_for_service('/gazebo/pause_physics_dr') rospy.wait_for_service('/gazebo/unpause_physics_dr') pause_physics = ServiceProxyWrapper('/gazebo/pause_physics_dr', Empty) unpause_physics = ServiceProxyWrapper('/gazebo/unpause_physics_dr', Empty) if preset_file_success: preset_location = os.path.join(CUSTOM_FILES_PATH, "preset.py") preset_location += ":graph_manager" graph_manager = short_dynamic_import(preset_location, ignore_module_case=True) logger.info("Using custom preset file!") else: graph_manager, _ = get_graph_manager( hp_dict=sm_hyperparams_dict, agent_list=agent_list, run_phase_subject=run_phase_subject, enable_domain_randomization=enable_domain_randomization, pause_physics=pause_physics, unpause_physics=unpause_physics) # If num_episodes_between_training is smaller than num_workers then cancel worker early. episode_steps_per_rollout = graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps # Reduce number of workers if allocated more than num_episodes_between_training if args.num_workers > episode_steps_per_rollout: logger.info( "Excess worker allocated. Reducing from {} to {}...".format( args.num_workers, episode_steps_per_rollout)) args.num_workers = episode_steps_per_rollout if args.rollout_idx >= episode_steps_per_rollout or args.rollout_idx >= args.num_workers: err_msg_format = "Exiting excess worker..." err_msg_format += "(rollout_idx[{}] >= num_workers[{}] or num_episodes_between_training[{}])" logger.info( err_msg_format.format(args.rollout_idx, args.num_workers, episode_steps_per_rollout)) # Close the down the job utils.cancel_simulation_job() memory_backend_params = DeepRacerRedisPubSubMemoryBackendParameters( redis_address=redis_ip, redis_port=6379, run_type=str(RunType.ROLLOUT_WORKER), channel=args.s3_prefix, num_workers=args.num_workers, rollout_idx=args.rollout_idx) graph_manager.memory_backend_params = memory_backend_params checkpoint_dict = {'agent': checkpoint} ds_params_instance = S3BotoDataStoreParameters( checkpoint_dict=checkpoint_dict) graph_manager.data_store = S3BotoDataStore(ds_params_instance, graph_manager) task_parameters = TaskParameters() task_parameters.checkpoint_restore_path = args.checkpoint_dir rollout_worker(graph_manager=graph_manager, num_workers=args.num_workers, rollout_idx=args.rollout_idx, task_parameters=task_parameters, simtrace_video_s3_writers=simtrace_video_s3_writers, pause_physics=pause_physics, unpause_physics=unpause_physics)
def main(): """ Main function for evaluation worker """ parser = argparse.ArgumentParser() parser.add_argument('-p', '--preset', help="(string) Name of a preset to run \ (class name from the 'presets' directory.)", type=str, required=False) parser.add_argument('--s3_bucket', help='list(string) S3 bucket', type=str, nargs='+', default=rospy.get_param("MODEL_S3_BUCKET", ["gsaur-test"])) parser.add_argument('--s3_prefix', help='list(string) S3 prefix', type=str, nargs='+', default=rospy.get_param("MODEL_S3_PREFIX", ["sagemaker"])) parser.add_argument('--aws_region', help='(string) AWS region', type=str, default=rospy.get_param("AWS_REGION", "us-east-1")) parser.add_argument('--number_of_trials', help='(integer) Number of trials', type=int, default=int(rospy.get_param("NUMBER_OF_TRIALS", 10))) parser.add_argument( '-c', '--local_model_directory', help='(string) Path to a folder containing a checkpoint \ to restore the model from.', type=str, default='./checkpoint') parser.add_argument('--number_of_resets', help='(integer) Number of resets', type=int, default=int(rospy.get_param("NUMBER_OF_RESETS", 0))) parser.add_argument('--penalty_seconds', help='(float) penalty second', type=float, default=float(rospy.get_param("PENALTY_SECONDS", 2.0))) parser.add_argument('--job_type', help='(string) job type', type=str, default=rospy.get_param("JOB_TYPE", "EVALUATION")) parser.add_argument('--is_continuous', help='(boolean) is continous after lap completion', type=bool, default=utils.str2bool( rospy.get_param("IS_CONTINUOUS", False))) parser.add_argument('--race_type', help='(string) Race type', type=str, default=rospy.get_param("RACE_TYPE", "TIME_TRIAL")) parser.add_argument('--off_track_penalty', help='(float) off track penalty second', type=float, default=float(rospy.get_param("OFF_TRACK_PENALTY", 2.0))) parser.add_argument('--collision_penalty', help='(float) collision penalty second', type=float, default=float(rospy.get_param("COLLISION_PENALTY", 5.0))) args = parser.parse_args() arg_s3_bucket = args.s3_bucket arg_s3_prefix = args.s3_prefix logger.info("S3 bucket: %s \n S3 prefix: %s", arg_s3_bucket, arg_s3_prefix) metrics_s3_buckets = rospy.get_param('METRICS_S3_BUCKET') metrics_s3_object_keys = rospy.get_param('METRICS_S3_OBJECT_KEY') arg_s3_bucket, arg_s3_prefix = utils.force_list( arg_s3_bucket), utils.force_list(arg_s3_prefix) metrics_s3_buckets = utils.force_list(metrics_s3_buckets) metrics_s3_object_keys = utils.force_list(metrics_s3_object_keys) validate_list = [ arg_s3_bucket, arg_s3_prefix, metrics_s3_buckets, metrics_s3_object_keys ] simtrace_s3_bucket = rospy.get_param('SIMTRACE_S3_BUCKET', None) mp4_s3_bucket = rospy.get_param('MP4_S3_BUCKET', None) if simtrace_s3_bucket: simtrace_s3_object_prefix = rospy.get_param('SIMTRACE_S3_PREFIX') simtrace_s3_bucket = utils.force_list(simtrace_s3_bucket) simtrace_s3_object_prefix = utils.force_list(simtrace_s3_object_prefix) validate_list.extend([simtrace_s3_bucket, simtrace_s3_object_prefix]) if mp4_s3_bucket: mp4_s3_object_prefix = rospy.get_param('MP4_S3_OBJECT_PREFIX') mp4_s3_bucket = utils.force_list(mp4_s3_bucket) mp4_s3_object_prefix = utils.force_list(mp4_s3_object_prefix) validate_list.extend([mp4_s3_bucket, mp4_s3_object_prefix]) if not all([lambda x: len(x) == len(validate_list[0]), validate_list]): log_and_exit( "Eval worker error: Incorrect arguments passed: {}".format( validate_list), SIMAPP_SIMULATION_WORKER_EXCEPTION, SIMAPP_EVENT_ERROR_CODE_500) if args.number_of_resets != 0 and args.number_of_resets < MIN_RESET_COUNT: raise GenericRolloutException( "number of resets is less than {}".format(MIN_RESET_COUNT)) # Instantiate Cameras if len(arg_s3_bucket) == 1: configure_camera(namespaces=['racecar']) else: configure_camera(namespaces=[ 'racecar_{}'.format(str(agent_index)) for agent_index in range(len(arg_s3_bucket)) ]) agent_list = list() s3_bucket_dict = dict() s3_prefix_dict = dict() checkpoint_dict = dict() simtrace_video_s3_writers = [] start_positions = get_start_positions(len(arg_s3_bucket)) done_condition = utils.str_to_done_condition( rospy.get_param("DONE_CONDITION", any)) park_positions = utils.pos_2d_str_to_list( rospy.get_param("PARK_POSITIONS", [])) # if not pass in park positions for all done condition case, use default if not park_positions: park_positions = [DEFAULT_PARK_POSITION for _ in arg_s3_bucket] for agent_index, _ in enumerate(arg_s3_bucket): agent_name = 'agent' if len(arg_s3_bucket) == 1 else 'agent_{}'.format( str(agent_index)) racecar_name = 'racecar' if len( arg_s3_bucket) == 1 else 'racecar_{}'.format(str(agent_index)) s3_bucket_dict[agent_name] = arg_s3_bucket[agent_index] s3_prefix_dict[agent_name] = arg_s3_prefix[agent_index] # download model metadata model_metadata = ModelMetadata( bucket=arg_s3_bucket[agent_index], s3_key=get_s3_key(arg_s3_prefix[agent_index], MODEL_METADATA_S3_POSTFIX), region_name=args.aws_region, local_path=MODEL_METADATA_LOCAL_PATH_FORMAT.format(agent_name)) model_metadata_info = model_metadata.get_model_metadata_info() version = model_metadata_info[ModelMetadataKeys.VERSION.value] # checkpoint s3 instance checkpoint = Checkpoint(bucket=arg_s3_bucket[agent_index], s3_prefix=arg_s3_prefix[agent_index], region_name=args.aws_region, agent_name=agent_name, checkpoint_dir=args.local_model_directory) # make coach checkpoint compatible if version < SIMAPP_VERSION_2 and not checkpoint.rl_coach_checkpoint.is_compatible( ): checkpoint.rl_coach_checkpoint.make_compatible( checkpoint.syncfile_ready) # get best model checkpoint string model_checkpoint_name = checkpoint.deepracer_checkpoint_json.get_deepracer_best_checkpoint( ) # Select the best checkpoint model by uploading rl coach .coach_checkpoint file checkpoint.rl_coach_checkpoint.update( model_checkpoint_name=model_checkpoint_name, s3_kms_extra_args=utils.get_s3_kms_extra_args()) checkpoint_dict[agent_name] = checkpoint agent_config = { 'model_metadata': model_metadata, ConfigParams.CAR_CTRL_CONFIG.value: { ConfigParams.LINK_NAME_LIST.value: [ link_name.replace('racecar', racecar_name) for link_name in LINK_NAMES ], ConfigParams.VELOCITY_LIST.value: [ velocity_topic.replace('racecar', racecar_name) for velocity_topic in VELOCITY_TOPICS ], ConfigParams.STEERING_LIST.value: [ steering_topic.replace('racecar', racecar_name) for steering_topic in STEERING_TOPICS ], ConfigParams.CHANGE_START.value: utils.str2bool(rospy.get_param('CHANGE_START_POSITION', False)), ConfigParams.ALT_DIR.value: utils.str2bool( rospy.get_param('ALTERNATE_DRIVING_DIRECTION', False)), ConfigParams.MODEL_METADATA.value: model_metadata, ConfigParams.REWARD.value: reward_function, ConfigParams.AGENT_NAME.value: racecar_name, ConfigParams.VERSION.value: version, ConfigParams.NUMBER_OF_RESETS.value: args.number_of_resets, ConfigParams.PENALTY_SECONDS.value: args.penalty_seconds, ConfigParams.NUMBER_OF_TRIALS.value: args.number_of_trials, ConfigParams.IS_CONTINUOUS.value: args.is_continuous, ConfigParams.RACE_TYPE.value: args.race_type, ConfigParams.COLLISION_PENALTY.value: args.collision_penalty, ConfigParams.OFF_TRACK_PENALTY.value: args.off_track_penalty, ConfigParams.START_POSITION.value: start_positions[agent_index], ConfigParams.DONE_CONDITION.value: done_condition } } metrics_s3_config = { MetricsS3Keys.METRICS_BUCKET.value: metrics_s3_buckets[agent_index], MetricsS3Keys.METRICS_KEY.value: metrics_s3_object_keys[agent_index], # Replaced rospy.get_param('AWS_REGION') to be equal to the argument being passed # or default argument set MetricsS3Keys.REGION.value: args.aws_region } aws_region = rospy.get_param('AWS_REGION', args.aws_region) if simtrace_s3_bucket: simtrace_video_s3_writers.append( SimtraceVideo( upload_type=SimtraceVideoNames.SIMTRACE_EVAL.value, bucket=simtrace_s3_bucket[agent_index], s3_prefix=simtrace_s3_object_prefix[agent_index], region_name=aws_region, local_path=SIMTRACE_EVAL_LOCAL_PATH_FORMAT.format( agent_name))) if mp4_s3_bucket: simtrace_video_s3_writers.extend([ SimtraceVideo( upload_type=SimtraceVideoNames.PIP.value, bucket=mp4_s3_bucket[agent_index], s3_prefix=mp4_s3_object_prefix[agent_index], region_name=aws_region, local_path=CAMERA_PIP_MP4_LOCAL_PATH_FORMAT.format( agent_name)), SimtraceVideo( upload_type=SimtraceVideoNames.DEGREE45.value, bucket=mp4_s3_bucket[agent_index], s3_prefix=mp4_s3_object_prefix[agent_index], region_name=aws_region, local_path=CAMERA_45DEGREE_LOCAL_PATH_FORMAT.format( agent_name)), SimtraceVideo( upload_type=SimtraceVideoNames.TOPVIEW.value, bucket=mp4_s3_bucket[agent_index], s3_prefix=mp4_s3_object_prefix[agent_index], region_name=aws_region, local_path=CAMERA_TOPVIEW_LOCAL_PATH_FORMAT.format( agent_name)) ]) run_phase_subject = RunPhaseSubject() agent_list.append( create_rollout_agent( agent_config, EvalMetrics(agent_name, metrics_s3_config, args.is_continuous), run_phase_subject)) agent_list.append(create_obstacles_agent()) agent_list.append(create_bot_cars_agent()) # ROS service to indicate all the robomaker markov packages are ready for consumption signal_robomaker_markov_package_ready() PhaseObserver('/agent/training_phase', run_phase_subject) enable_domain_randomization = utils.str2bool( rospy.get_param('ENABLE_DOMAIN_RANDOMIZATION', False)) sm_hyperparams_dict = {} # Make the clients that will allow us to pause and unpause the physics rospy.wait_for_service('/gazebo/pause_physics_dr') rospy.wait_for_service('/gazebo/unpause_physics_dr') pause_physics = ServiceProxyWrapper('/gazebo/pause_physics_dr', Empty) unpause_physics = ServiceProxyWrapper('/gazebo/unpause_physics_dr', Empty) graph_manager, _ = get_graph_manager( hp_dict=sm_hyperparams_dict, agent_list=agent_list, run_phase_subject=run_phase_subject, enable_domain_randomization=enable_domain_randomization, done_condition=done_condition, pause_physics=pause_physics, unpause_physics=unpause_physics) ds_params_instance = S3BotoDataStoreParameters( checkpoint_dict=checkpoint_dict) graph_manager.data_store = S3BotoDataStore(params=ds_params_instance, graph_manager=graph_manager, ignore_lock=True) graph_manager.env_params.seed = 0 task_parameters = TaskParameters() task_parameters.checkpoint_restore_path = args.local_model_directory evaluation_worker(graph_manager=graph_manager, number_of_trials=args.number_of_trials, task_parameters=task_parameters, simtrace_video_s3_writers=simtrace_video_s3_writers, is_continuous=args.is_continuous, park_positions=park_positions, race_type=args.race_type, pause_physics=pause_physics, unpause_physics=unpause_physics)