# # Unity ML-Agents Toolkit # ## ML-Agent Learning """Launches trainers for each External Brains in a Unity Environment.""" import os import sys import threading from typing import Dict, Optional, Set, List from collections import defaultdict import numpy as np from mlagents.tf_utils import tf from mlagents_envs.logging_util import get_logger from mlagents.trainers.env_manager import EnvManager from mlagents_envs.exception import ( UnityEnvironmentException, UnityCommunicationException, ) from mlagents.trainers.sampler_class import SamplerManager from mlagents_envs.timers import hierarchical_timer, timed from mlagents.trainers.trainer import Trainer from mlagents.trainers.meta_curriculum import MetaCurriculum from mlagents.trainers.trainer_util import TrainerFactory from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers from mlagents.trainers.agent_processor import AgentManager class TrainerController(object): def __init__( self, trainer_factory: TrainerFactory, model_path: str, summaries_dir: str, run_id: str, save_freq: int, meta_curriculum: Optional[MetaCurriculum], train: bool, training_seed: int, sampler_manager: SamplerManager, resampling_interval: Optional[int], ): """ :param model_path: Path to save the model. :param summaries_dir: Folder to save training summaries. :param run_id: The sub-directory name for model and summary statistics :param save_freq: Frequency at which to save model :param meta_curriculum: MetaCurriculum object which stores information about all curricula. :param train: Whether to train model, or only run inference. :param training_seed: Seed to use for Numpy and Tensorflow random number generation. :param sampler_manager: SamplerManager object handles samplers for resampling the reset parameters. :param resampling_interval: Specifies number of simulation steps after which reset parameters are resampled. :param threaded: Whether or not to run trainers in a separate thread. Disable for testing/debugging. """ self.trainers: Dict[str, Trainer] = {} self.brain_name_to_identifier: Dict[str, Set] = defaultdict(set) self.trainer_factory = trainer_factory self.model_path = model_path self.summaries_dir = summaries_dir self.logger = get_logger(__name__) self.run_id = run_id self.save_freq = save_freq self.train_model = train self.meta_curriculum = meta_curriculum self.sampler_manager = sampler_manager self.resampling_interval = resampling_interval self.trainer_threads: List[threading.Thread] = [] self.kill_trainers = False np.random.seed(training_seed) tf.set_random_seed(training_seed) def _get_measure_vals(self): brain_names_to_measure_vals = {} if self.meta_curriculum: for ( brain_name, curriculum, ) in self.meta_curriculum.brains_to_curricula.items(): # Skip brains that are in the metacurriculum but no trainer yet. if brain_name not in self.trainers: continue if curriculum.measure == "progress": measure_val = self.trainers[brain_name].get_step / float( 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 else: for brain_name, trainer in self.trainers.items(): measure_val = np.mean(trainer.reward_buffer) brain_names_to_measure_vals[brain_name] = measure_val return brain_names_to_measure_vals @timed def _save_model(self): """ Saves current model to checkpoint folder. """ for brain_name in self.trainers.keys(): for name_behavior_id in self.brain_name_to_identifier[brain_name]: self.trainers[brain_name].save_model(name_behavior_id) self.logger.info("Saved Model") def _save_model_when_interrupted(self): self.logger.info( "Learning was interrupted. Please wait while the graph is generated." ) self._save_model() def _export_graph(self): """ Exports latest saved models to .nn format for Unity embedding. """ for brain_name in self.trainers.keys(): for name_behavior_id in self.brain_name_to_identifier[brain_name]: self.trainers[brain_name].export_model(name_behavior_id) @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) ) @timed def _reset_env(self, env: EnvManager) -> None: """Resets the environment. Returns: A Data structure corresponding to the initial reset state of the environment. """ sampled_reset_param = self.sampler_manager.sample_all() new_meta_curriculum_config = ( self.meta_curriculum.get_config() if self.meta_curriculum else {} ) sampled_reset_param.update(new_meta_curriculum_config) env.reset(config=sampled_reset_param) def _should_save_model(self, global_step: int) -> bool: return ( global_step % self.save_freq == 0 and global_step != 0 and self.train_model ) def _not_done_training(self) -> bool: return ( any(t.should_still_train for t in self.trainers.values()) or not self.train_model ) or len(self.trainers) == 0 def _create_trainer_and_manager( self, env_manager: EnvManager, name_behavior_id: str ) -> None: parsed_behavior_id = BehaviorIdentifiers.from_name_behavior_id(name_behavior_id) brain_name = parsed_behavior_id.brain_name trainerthread = None try: trainer = self.trainers[brain_name] except KeyError: trainer = self.trainer_factory.generate(brain_name) self.trainers[brain_name] = trainer if trainer.threaded: # Only create trainer thread for new trainers trainerthread = threading.Thread( target=self.trainer_update_func, args=(trainer,), daemon=True ) self.trainer_threads.append(trainerthread) policy = trainer.create_policy( parsed_behavior_id, env_manager.external_brains[name_behavior_id] ) trainer.add_policy(parsed_behavior_id, policy) agent_manager = AgentManager( policy, name_behavior_id, trainer.stats_reporter, trainer.parameters.get("time_horizon", sys.maxsize), threaded=trainer.threaded, ) env_manager.set_agent_manager(name_behavior_id, agent_manager) env_manager.set_policy(name_behavior_id, policy) self.brain_name_to_identifier[brain_name].add(name_behavior_id) trainer.publish_policy_queue(agent_manager.policy_queue) trainer.subscribe_trajectory_queue(agent_manager.trajectory_queue) # Only start new trainers if trainerthread is not None: trainerthread.start() def _create_trainers_and_managers( self, env_manager: EnvManager, behavior_ids: Set[str] ) -> None: for behavior_id in behavior_ids: self._create_trainer_and_manager(env_manager, behavior_id) @timed def start_learning(self, env_manager: EnvManager) -> None: self._create_model_path(self.model_path) tf.reset_default_graph() global_step = 0 last_brain_behavior_ids: Set[str] = set() try: # Initial reset self._reset_env(env_manager) while self._not_done_training(): external_brain_behavior_ids = set(env_manager.external_brains.keys()) new_behavior_ids = external_brain_behavior_ids - last_brain_behavior_ids self._create_trainers_and_managers(env_manager, new_behavior_ids) last_brain_behavior_ids = external_brain_behavior_ids n_steps = self.advance(env_manager) for _ in range(n_steps): global_step += 1 self.reset_env_if_ready(env_manager, global_step) if self._should_save_model(global_step): self._save_model() # Stop advancing trainers self.kill_trainers = True # Final save Tensorflow model if global_step != 0 and self.train_model: self._save_model() except ( KeyboardInterrupt, UnityCommunicationException, UnityEnvironmentException, ) as ex: self.kill_trainers = True if self.train_model: self._save_model_when_interrupted() if isinstance(ex, KeyboardInterrupt): pass else: # If the environment failed, we want to make sure to raise # the exception so we exit the process with an return code of 1. raise ex if self.train_model: self._export_graph() def end_trainer_episodes( self, env: EnvManager, lessons_incremented: Dict[str, bool] ) -> None: self._reset_env(env) # Reward buffers reset takes place only for curriculum learning # else no reset. for trainer in self.trainers.values(): trainer.end_episode() for brain_name, changed in lessons_incremented.items(): if changed: self.trainers[brain_name].reward_buffer.clear() def reset_env_if_ready(self, env: EnvManager, steps: int) -> None: 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 ) else: lessons_incremented = {} # If any lessons were incremented or the environment is # ready to be reset meta_curriculum_reset = any(lessons_incremented.values()) # Check if we are performing generalization training and we have finished the # specified number of steps for the lesson generalization_reset = ( not self.sampler_manager.is_empty() and (steps != 0) and (self.resampling_interval) and (steps % self.resampling_interval == 0) ) if meta_curriculum_reset or generalization_reset: self.end_trainer_episodes(env, lessons_incremented) @timed def advance(self, env: EnvManager) -> int: # Get steps with hierarchical_timer("env_step"): num_steps = env.advance() # Report current lesson if self.meta_curriculum: for brain_name, curr in self.meta_curriculum.brains_to_curricula.items(): if brain_name in self.trainers: self.trainers[brain_name].stats_reporter.set_stat( "Environment/Lesson", curr.lesson_num ) for trainer in self.trainers.values(): if not trainer.threaded: with hierarchical_timer("trainer_advance"): trainer.advance() return num_steps def trainer_update_func(self, trainer: Trainer) -> None: while not self.kill_trainers: with hierarchical_timer("trainer_advance"): trainer.advance()