# ## ML-Agent Learning (SAC) # Contains an implementation of SAC as described in https://arxiv.org/abs/1801.01290 # and implemented in https://github.com/hill-a/stable-baselines from collections import defaultdict from typing import Dict import os import numpy as np from mlagents_envs.logging_util import get_logger from mlagents_envs.timers import timed from mlagents.trainers.policy.tf_policy import TFPolicy from mlagents.trainers.policy.nn_policy import NNPolicy from mlagents.trainers.sac.optimizer import SACOptimizer from mlagents.trainers.trainer.rl_trainer import RLTrainer from mlagents.trainers.trajectory import Trajectory, SplitObservations from mlagents.trainers.brain import BrainParameters from mlagents.trainers.exception import UnityTrainerException from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers logger = get_logger(__name__) BUFFER_TRUNCATE_PERCENT = 0.8 class SACTrainer(RLTrainer): """ The SACTrainer is an implementation of the SAC algorithm, with support for discrete actions and recurrent networks. """ def __init__( self, brain_name: str, reward_buff_cap: int, trainer_parameters: dict, training: bool, load: bool, seed: int, run_id: str, ): """ Responsible for collecting experiences and training SAC model. :param brain_name: The name of the brain associated with trainer config :param reward_buff_cap: Max reward history to track in the reward buffer :param trainer_parameters: The parameters for the trainer (dictionary). :param training: Whether the trainer is set for training. :param load: Whether the model should be loaded. :param seed: The seed the model will be initialized with :param run_id: The The identifier of the current run """ super().__init__( brain_name, trainer_parameters, training, run_id, reward_buff_cap ) self.param_keys = [ "batch_size", "buffer_size", "buffer_init_steps", "hidden_units", "learning_rate", "init_entcoef", "max_steps", "normalize", "num_update", "num_layers", "time_horizon", "sequence_length", "summary_freq", "tau", "use_recurrent", "summary_path", "memory_size", "model_path", "reward_signals", ] self._check_param_keys() self.load = load self.seed = seed self.policy: NNPolicy = None # type: ignore self.optimizer: SACOptimizer = None # type: ignore self.step = 0 self.train_interval = ( trainer_parameters["train_interval"] if "train_interval" in trainer_parameters else 1 ) self.reward_signal_updates_per_train = ( trainer_parameters["reward_signals"]["reward_signal_num_update"] if "reward_signal_num_update" in trainer_parameters["reward_signals"] else trainer_parameters["num_update"] ) self.checkpoint_replay_buffer = ( trainer_parameters["save_replay_buffer"] if "save_replay_buffer" in trainer_parameters else False ) def _check_param_keys(self): super()._check_param_keys() # Check that batch size is greater than sequence length. Else, throw # an exception. if ( self.trainer_parameters["sequence_length"] > self.trainer_parameters["batch_size"] and self.trainer_parameters["use_recurrent"] ): raise UnityTrainerException( "batch_size must be greater than or equal to sequence_length when use_recurrent is True." ) def save_model(self, name_behavior_id: str) -> None: """ Saves the model. Overrides the default save_model since we want to save the replay buffer as well. """ self.policy.save_model(self.get_step) if self.checkpoint_replay_buffer: self.save_replay_buffer() def save_replay_buffer(self) -> None: """ Save the training buffer's update buffer to a pickle file. """ filename = os.path.join( self.trainer_parameters["model_path"], "last_replay_buffer.hdf5" ) logger.info("Saving Experience Replay Buffer to {}".format(filename)) with open(filename, "wb") as file_object: self.update_buffer.save_to_file(file_object) def load_replay_buffer(self) -> None: """ Loads the last saved replay buffer from a file. """ filename = os.path.join( self.trainer_parameters["model_path"], "last_replay_buffer.hdf5" ) logger.info("Loading Experience Replay Buffer from {}".format(filename)) with open(filename, "rb+") as file_object: self.update_buffer.load_from_file(file_object) logger.info( "Experience replay buffer has {} experiences.".format( self.update_buffer.num_experiences ) ) def _process_trajectory(self, trajectory: Trajectory) -> None: """ Takes a trajectory and processes it, putting it into the replay buffer. """ super()._process_trajectory(trajectory) last_step = trajectory.steps[-1] agent_id = trajectory.agent_id # All the agents should have the same ID agent_buffer_trajectory = trajectory.to_agentbuffer() # Update the normalization if self.is_training: self.policy.update_normalization(agent_buffer_trajectory["vector_obs"]) # Evaluate all reward functions for reporting purposes self.collected_rewards["environment"][agent_id] += np.sum( agent_buffer_trajectory["environment_rewards"] ) for name, reward_signal in self.optimizer.reward_signals.items(): evaluate_result = reward_signal.evaluate_batch( agent_buffer_trajectory ).scaled_reward # Report the reward signals self.collected_rewards[name][agent_id] += np.sum(evaluate_result) # Get all value estimates for reporting purposes value_estimates, _ = self.optimizer.get_trajectory_value_estimates( agent_buffer_trajectory, trajectory.next_obs, trajectory.done_reached ) for name, v in value_estimates.items(): self._stats_reporter.add_stat( self.optimizer.reward_signals[name].value_name, np.mean(v) ) # Bootstrap using the last step rather than the bootstrap step if max step is reached. # Set last element to duplicate obs and remove dones. if last_step.max_step: vec_vis_obs = SplitObservations.from_observations(last_step.obs) for i, obs in enumerate(vec_vis_obs.visual_observations): agent_buffer_trajectory["next_visual_obs%d" % i][-1] = obs if vec_vis_obs.vector_observations.size > 1: agent_buffer_trajectory["next_vector_in"][ -1 ] = vec_vis_obs.vector_observations agent_buffer_trajectory["done"][-1] = False # Append to update buffer agent_buffer_trajectory.resequence_and_append( self.update_buffer, training_length=self.policy.sequence_length ) if trajectory.done_reached: self._update_end_episode_stats(agent_id, self.optimizer) def _is_ready_update(self) -> bool: """ Returns whether or not the trainer has enough elements to run update model :return: A boolean corresponding to whether or not update_model() can be run """ return ( self.update_buffer.num_experiences >= self.trainer_parameters["batch_size"] and self.step >= self.trainer_parameters["buffer_init_steps"] ) @timed def _update_policy(self) -> None: """ If train_interval is met, update the SAC policy given the current reward signals. If reward_signal_train_interval is met, update the reward signals from the buffer. """ if self.step % self.train_interval == 0: self.update_sac_policy() self.update_reward_signals() def create_policy( self, parsed_behavior_id: BehaviorIdentifiers, brain_parameters: BrainParameters ) -> TFPolicy: policy = NNPolicy( self.seed, brain_parameters, self.trainer_parameters, self.is_training, self.load, tanh_squash=True, reparameterize=True, create_tf_graph=False, ) # Load the replay buffer if load if self.load and self.checkpoint_replay_buffer: try: self.load_replay_buffer() except (AttributeError, FileNotFoundError): logger.warning( "Replay buffer was unable to load, starting from scratch." ) logger.debug( "Loaded update buffer with {} sequences".format( self.update_buffer.num_experiences ) ) return policy def update_sac_policy(self) -> None: """ Uses demonstration_buffer to update the policy. The reward signal generators are updated using different mini batches. If we want to imitate http://arxiv.org/abs/1809.02925 and similar papers, where the policy is updated N times, then the reward signals are updated N times, then reward_signal_updates_per_train is greater than 1 and the reward signals are not updated in parallel. """ self.cumulative_returns_since_policy_update.clear() n_sequences = max( int(self.trainer_parameters["batch_size"] / self.policy.sequence_length), 1 ) num_updates = self.trainer_parameters["num_update"] batch_update_stats: Dict[str, list] = defaultdict(list) for _ in range(num_updates): logger.debug("Updating SAC policy at step {}".format(self.step)) buffer = self.update_buffer if ( self.update_buffer.num_experiences >= self.trainer_parameters["batch_size"] ): sampled_minibatch = buffer.sample_mini_batch( self.trainer_parameters["batch_size"], sequence_length=self.policy.sequence_length, ) # Get rewards for each reward for name, signal in self.optimizer.reward_signals.items(): sampled_minibatch[ "{}_rewards".format(name) ] = signal.evaluate_batch(sampled_minibatch).scaled_reward update_stats = self.optimizer.update(sampled_minibatch, n_sequences) for stat_name, value in update_stats.items(): batch_update_stats[stat_name].append(value) # Truncate update buffer if neccessary. Truncate more than we need to to avoid truncating # a large buffer at each update. if self.update_buffer.num_experiences > self.trainer_parameters["buffer_size"]: self.update_buffer.truncate( int(self.trainer_parameters["buffer_size"] * BUFFER_TRUNCATE_PERCENT) ) for stat, stat_list in batch_update_stats.items(): self._stats_reporter.add_stat(stat, np.mean(stat_list)) if self.optimizer.bc_module: update_stats = self.optimizer.bc_module.update() for stat, val in update_stats.items(): self._stats_reporter.add_stat(stat, val) def update_reward_signals(self) -> None: """ Iterate through the reward signals and update them. Unlike in PPO, do it separate from the policy so that it can be done at a different interval. This function should only be used to simulate http://arxiv.org/abs/1809.02925 and similar papers, where the policy is updated N times, then the reward signals are updated N times. Normally, the reward signal and policy are updated in parallel. """ buffer = self.update_buffer num_updates = self.reward_signal_updates_per_train n_sequences = max( int(self.trainer_parameters["batch_size"] / self.policy.sequence_length), 1 ) batch_update_stats: Dict[str, list] = defaultdict(list) for _ in range(num_updates): # Get minibatches for reward signal update if needed reward_signal_minibatches = {} for name, signal in self.optimizer.reward_signals.items(): logger.debug("Updating {} at step {}".format(name, self.step)) # Some signals don't need a minibatch to be sampled - so we don't! if signal.update_dict: reward_signal_minibatches[name] = buffer.sample_mini_batch( self.trainer_parameters["batch_size"], sequence_length=self.policy.sequence_length, ) update_stats = self.optimizer.update_reward_signals( reward_signal_minibatches, n_sequences ) for stat_name, value in update_stats.items(): batch_update_stats[stat_name].append(value) for stat, stat_list in batch_update_stats.items(): self._stats_reporter.add_stat(stat, np.mean(stat_list)) def add_policy( self, parsed_behavior_id: BehaviorIdentifiers, policy: TFPolicy ) -> None: """ Adds policy to trainer. :param brain_parameters: specifications for policy construction """ if self.policy: logger.warning( "Your environment contains multiple teams, but {} doesn't support adversarial games. Enable self-play to \ train adversarial games.".format( self.__class__.__name__ ) ) if not isinstance(policy, NNPolicy): raise RuntimeError("Non-SACPolicy passed to SACTrainer.add_policy()") self.policy = policy self.optimizer = SACOptimizer(self.policy, self.trainer_parameters) for _reward_signal in self.optimizer.reward_signals.keys(): self.collected_rewards[_reward_signal] = defaultdict(lambda: 0) # Needed to resume loads properly self.step = policy.get_current_step() self.next_summary_step = self._get_next_summary_step() def get_policy(self, name_behavior_id: str) -> TFPolicy: """ Gets policy from trainer associated with name_behavior_id :param name_behavior_id: full identifier of policy """ return self.policy