# # Unity ML-Agents Toolkit # ## ML-Agent Learning """Launches trainers for each External Brains in a Unity Environment.""" import os import logging import yaml import re import numpy as np import tensorflow as tf from tensorflow.python.tools import freeze_graph from mlagents.envs.environment import UnityEnvironment from mlagents.envs.exception import UnityEnvironmentException from mlagents.trainers.ppo.trainer import PPOTrainer from mlagents.trainers.bc.offline_trainer import OfflineBCTrainer from mlagents.trainers.bc.online_trainer import OnlineBCTrainer from mlagents.trainers.meta_curriculum import MetaCurriculum from mlagents.trainers.exception import MetaCurriculumError class TrainerController(object): def __init__(self, env_path, run_id, save_freq, curriculum_folder, fast_simulation, load, train, worker_id, keep_checkpoints, lesson, seed, docker_target_name, trainer_config_path, no_graphics): """ :param env_path: Location to the environment executable to be loaded. :param run_id: The sub-directory name for model and summary statistics :param save_freq: Frequency at which to save model :param curriculum_folder: Folder containing JSON curriculums for the environment. :param fast_simulation: Whether to run the game at training speed. :param load: Whether to load the model or randomly initialize. :param train: Whether to train model, or only run inference. :param worker_id: Number to add to communication port (5005). Used for multi-environment :param keep_checkpoints: How many model checkpoints to keep. :param lesson: Start learning from this lesson. :param seed: Random seed used for training. :param docker_target_name: Name of docker volume that will contain all data. :param trainer_config_path: Fully qualified path to location of trainer configuration file. :param no_graphics: Whether to run the Unity simulator in no-graphics mode. """ if env_path is not None: # Strip out executable extensions if passed env_path = (env_path.strip() .replace('.app', '') .replace('.exe', '') .replace('.x86_64', '') .replace('.x86', '')) # Recognize and use docker volume if one is passed as an argument if not docker_target_name: self.docker_training = False self.trainer_config_path = trainer_config_path self.model_path = './models/{run_id}'.format(run_id=run_id) self.curriculum_folder = curriculum_folder self.summaries_dir = './summaries' else: self.docker_training = True self.trainer_config_path = \ '/{docker_target_name}/{trainer_config_path}'.format( docker_target_name=docker_target_name, trainer_config_path = trainer_config_path) self.model_path = '/{docker_target_name}/models/{run_id}'.format( docker_target_name=docker_target_name, run_id=run_id) if env_path is not None: env_path = '/{docker_target_name}/{env_name}'.format( docker_target_name=docker_target_name, env_name=env_path) if curriculum_folder is not None: self.curriculum_folder = \ '/{docker_target_name}/{curriculum_folder}'.format( docker_target_name=docker_target_name, curriculum_folder=curriculum_folder) self.summaries_dir = '/{docker_target_name}/summaries'.format( docker_target_name=docker_target_name) self.logger = logging.getLogger('mlagents.envs') self.run_id = run_id self.save_freq = save_freq self.lesson = lesson self.fast_simulation = fast_simulation self.load_model = load self.train_model = train self.worker_id = worker_id self.keep_checkpoints = keep_checkpoints self.trainers = {} self.seed = seed np.random.seed(self.seed) tf.set_random_seed(self.seed) self.env = UnityEnvironment(file_name=env_path, worker_id=self.worker_id, seed=self.seed, docker_training=self.docker_training, no_graphics=no_graphics) if env_path is None: self.env_name = 'editor_' + self.env.academy_name else: # Extract out name of environment self.env_name = os.path.basename(os.path.normpath(env_path)) if curriculum_folder is None: self.meta_curriculum = None else: self.meta_curriculum = MetaCurriculum(self.curriculum_folder, self.env._resetParameters) if self.meta_curriculum: for brain_name in self.meta_curriculum.brains_to_curriculums.keys(): if brain_name not in self.env.external_brain_names: raise MetaCurriculumError('One of the curriculums ' 'defined in ' + self.curriculum_folder + ' ' 'does not have a corresponding ' 'Brain. Check that the ' 'curriculum file has the same ' 'name as the Brain ' 'whose curriculum it defines.') def _get_measure_vals(self): if self.meta_curriculum: brain_names_to_measure_vals = {} for brain_name, curriculum \ in self.meta_curriculum.brains_to_curriculums.items(): if curriculum.measure == 'progress': measure_val = (self.trainers[brain_name].get_step / self.trainers[brain_name].get_max_steps) brain_names_to_measure_vals[brain_name] = measure_val elif curriculum.measure == 'reward': measure_val = np.mean(self.trainers[brain_name] .reward_buffer) brain_names_to_measure_vals[brain_name] = measure_val return brain_names_to_measure_vals else: return None def _save_model(self,steps=0): """ Saves current model to checkpoint folder. :param steps: Current number of steps in training process. :param saver: Tensorflow saver for session. """ for brain_name in self.trainers.keys(): self.trainers[brain_name].save_model() self.logger.info('Saved Model') def _export_graph(self): """ Exports latest saved models to .bytes format for Unity embedding. """ for brain_name in self.trainers.keys(): self.trainers[brain_name].export_model() def _initialize_trainers(self, trainer_config): """ Initialization of the trainers :param trainer_config: The configurations of the trainers """ trainer_parameters_dict = {} for brain_name in self.env.external_brain_names: trainer_parameters = trainer_config['default'].copy() trainer_parameters['summary_path'] = '{basedir}/{name}'.format( basedir=self.summaries_dir, name=str(self.run_id) + '_' + brain_name) trainer_parameters['model_path'] = '{basedir}/{name}'.format( basedir=self.model_path, name=brain_name) trainer_parameters['keep_checkpoints'] = self.keep_checkpoints if brain_name in trainer_config: _brain_key = brain_name while not isinstance(trainer_config[_brain_key], dict): _brain_key = trainer_config[_brain_key] for k in trainer_config[_brain_key]: trainer_parameters[k] = trainer_config[_brain_key][k] trainer_parameters_dict[brain_name] = trainer_parameters.copy() for brain_name in self.env.external_brain_names: if trainer_parameters_dict[brain_name]['trainer'] == 'offline_bc': self.trainers[brain_name] = OfflineBCTrainer( self.env.brains[brain_name], trainer_parameters_dict[brain_name], self.train_model, self.load_model, self.seed, self.run_id) elif trainer_parameters_dict[brain_name]['trainer'] == 'online_bc': self.trainers[brain_name] = OnlineBCTrainer( self.env.brains[brain_name], trainer_parameters_dict[brain_name], self.train_model, self.load_model, self.seed, self.run_id) elif trainer_parameters_dict[brain_name]['trainer'] == 'ppo': self.trainers[brain_name] = PPOTrainer( self.env.brains[brain_name], self.meta_curriculum .brains_to_curriculums[brain_name] .min_lesson_length if self.meta_curriculum else 0, trainer_parameters_dict[brain_name], self.train_model, self.load_model, self.seed, self.run_id) else: raise UnityEnvironmentException('The trainer config contains ' 'an unknown trainer type for ' 'brain {}' .format(brain_name)) def _load_config(self): try: with open(self.trainer_config_path) as data_file: trainer_config = yaml.load(data_file) return trainer_config except IOError: raise UnityEnvironmentException('Parameter file could not be found ' 'at {}.' .format(self.trainer_config_path)) except UnicodeDecodeError: raise UnityEnvironmentException('There was an error decoding ' 'Trainer Config from this path : {}' .format(self.trainer_config_path)) @staticmethod def _create_model_path(model_path): try: if not os.path.exists(model_path): os.makedirs(model_path) except Exception: raise UnityEnvironmentException('The folder {} containing the ' 'generated model could not be ' 'accessed. Please make sure the ' 'permissions are set correctly.' .format(model_path)) def _reset_env(self): """Resets the environment. Returns: A Data structure corresponding to the initial reset state of the environment. """ if self.meta_curriculum is not None: return self.env.reset(config=self.meta_curriculum.get_config(), train_mode=self.fast_simulation) else: return self.env.reset(train_mode=self.fast_simulation) def start_learning(self): # TODO: Should be able to start learning at different lesson numbers # for each curriculum. if self.meta_curriculum is not None: self.meta_curriculum.set_all_curriculums_to_lesson_num(self.lesson) trainer_config = self._load_config() self._create_model_path(self.model_path) tf.reset_default_graph() # Prevent a single session from taking all GPU memory. self._initialize_trainers(trainer_config) for _, t in self.trainers.items(): self.logger.info(t) global_step = 0 # This is only for saving the model curr_info = self._reset_env() if self.train_model: for brain_name, trainer in self.trainers.items(): trainer.write_tensorboard_text('Hyperparameters', trainer.parameters) try: while any([t.get_step <= t.get_max_steps \ for k, t in self.trainers.items()]) \ or not self.train_model: if self.meta_curriculum: # Get the sizes of the reward buffers. reward_buff_sizes = {k:len(t.reward_buffer) \ for (k,t) in self.trainers.items()} # Attempt to increment the lessons of the brains who # were ready. lessons_incremented = \ self.meta_curriculum.increment_lessons( self._get_measure_vals(), reward_buff_sizes=reward_buff_sizes) # If any lessons were incremented or the environment is # ready to be reset if (self.meta_curriculum and any(lessons_incremented.values())): curr_info = self._reset_env() for brain_name, trainer in self.trainers.items(): trainer.end_episode() for brain_name, changed in lessons_incremented.items(): if changed: self.trainers[brain_name].reward_buffer.clear() elif self.env.global_done: curr_info = self._reset_env() for brain_name, trainer in self.trainers.items(): trainer.end_episode() # Decide and take an action take_action_vector, \ take_action_memories, \ take_action_text, \ take_action_value, \ take_action_outputs \ = {}, {}, {}, {}, {} for brain_name, trainer in self.trainers.items(): (take_action_vector[brain_name], take_action_memories[brain_name], take_action_text[brain_name], take_action_value[brain_name], take_action_outputs[brain_name]) = \ trainer.take_action(curr_info) new_info = self.env.step(vector_action=take_action_vector, memory=take_action_memories, text_action=take_action_text, value=take_action_value) for brain_name, trainer in self.trainers.items(): trainer.add_experiences(curr_info, new_info, take_action_outputs[brain_name]) trainer.process_experiences(curr_info, new_info) if trainer.is_ready_update() and self.train_model \ and trainer.get_step <= trainer.get_max_steps: # Perform gradient descent with experience buffer trainer.update_policy() # Write training statistics to Tensorboard. if self.meta_curriculum is not None: trainer.write_summary( global_step, lesson_num=self.meta_curriculum .brains_to_curriculums[brain_name] .lesson_num) else: trainer.write_summary(global_step) if self.train_model \ and trainer.get_step <= trainer.get_max_steps: trainer.increment_step_and_update_last_reward() global_step += 1 if global_step % self.save_freq == 0 and global_step != 0 \ and self.train_model: # Save Tensorflow model self._save_model(steps=global_step) curr_info = new_info # Final save Tensorflow model if global_step != 0 and self.train_model: self._save_model(steps=global_step) except KeyboardInterrupt: print('--------------------------Now saving model--------------' '-----------') if self.train_model: self.logger.info('Learning was interrupted. Please wait ' 'while the graph is generated.') self._save_model(steps=global_step) pass self.env.close() if self.train_model: self._export_graph()