# ## 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, cast 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.transfer_policy import TransferPolicy from mlagents.trainers.policy.nn_policy import NNPolicy from mlagents.trainers.sac_transfer.optimizer import SACTransferOptimizer from mlagents.trainers.trainer.rl_trainer import RLTrainer from mlagents.trainers.trajectory import Trajectory, SplitObservations from mlagents.trainers.brain import BrainParameters from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers from mlagents.trainers.settings import TrainerSettings, SACSettings logger = get_logger(__name__) BUFFER_TRUNCATE_PERCENT = 0.8 class SACTransferTrainer(RLTrainer): """ The SACTransferTrainer is a variant of the SAC algorithm which supports model transfer """ def __init__( self, brain_name: str, reward_buff_cap: int, trainer_settings: TrainerSettings, training: bool, load: bool, seed: int, artifact_path: 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_settings: The parameters for the trainer. :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 artifact_path: The directory within which to store artifacts from this trainer. """ super().__init__( brain_name, trainer_settings, training, artifact_path, reward_buff_cap ) self.load = load self.seed = seed self.policy: NNPolicy = None # type: ignore self.optimizer: SACTransferOptimizer = None # type: ignore self.hyperparameters: SACSettings = cast( SACSettings, trainer_settings.hyperparameters ) self.step = 0 # Don't divide by zero self.update_steps = 1 self.reward_signal_update_steps = 1 self.steps_per_update = self.hyperparameters.steps_per_update self.reward_signal_steps_per_update = ( self.hyperparameters.reward_signal_steps_per_update ) self.checkpoint_replay_buffer = self.hyperparameters.save_replay_buffer 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.artifact_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.artifact_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"], agent_buffer_trajectory["next_vector_in"], 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.interrupted: 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_policy() can be run """ return ( self.update_buffer.num_experiences >= self.hyperparameters.batch_size and self.step >= self.hyperparameters.buffer_init_steps ) @timed def _update_policy(self) -> bool: """ Update the SAC policy and reward signals. The reward signal generators are updated using different mini batches. By default we 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. :return: Whether or not the policy was updated. """ policy_was_updated = self._update_sac_policy() self._update_reward_signals() return policy_was_updated def create_policy( self, parsed_behavior_id: BehaviorIdentifiers, brain_parameters: BrainParameters ) -> TFPolicy: policy = TransferPolicy( self.seed, brain_parameters, self.trainer_settings, self.is_training, self.artifact_path, 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) -> bool: """ Uses update_buffer to update the policy. We sample the update_buffer and update until the steps_per_update ratio is met. """ has_updated = False self.cumulative_returns_since_policy_update.clear() n_sequences = max( int(self.hyperparameters.batch_size / self.policy.sequence_length), 1 ) batch_update_stats: Dict[str, list] = defaultdict(list) while ( self.step - self.hyperparameters.buffer_init_steps ) / self.update_steps > self.steps_per_update: logger.debug("Updating SAC policy at step {}".format(self.step)) buffer = self.update_buffer if self.update_buffer.num_experiences >= self.hyperparameters.batch_size: sampled_minibatch = buffer.sample_mini_batch( self.hyperparameters.batch_size, sequence_length=self.policy.sequence_length, ) sampled_minibatch_bisim = buffer.sample_mini_batch( self.hyperparameters.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, sampled_minibatch_bisim, n_sequences) for stat_name, value in update_stats.items(): batch_update_stats[stat_name].append(value) self.update_steps += 1 for stat, stat_list in batch_update_stats.items(): self._stats_reporter.add_stat(stat, np.mean(stat_list)) has_updated = True 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) # 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.hyperparameters.buffer_size: self.update_buffer.truncate( int(self.hyperparameters.buffer_size * BUFFER_TRUNCATE_PERCENT) ) return has_updated 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 n_sequences = max( int(self.hyperparameters.batch_size / self.policy.sequence_length), 1 ) batch_update_stats: Dict[str, list] = defaultdict(list) while ( self.step - self.hyperparameters.buffer_init_steps ) / self.reward_signal_update_steps > self.reward_signal_steps_per_update: # 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.hyperparameters.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) self.reward_signal_update_steps += 1 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, TransferPolicy): raise RuntimeError("Non-TransferPolicy passed to SACTransferTrainer.add_policy()") self.policy = policy self.optimizer = SACTransferOptimizer(self.policy, self.trainer_settings) 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() # Assume steps were updated at the correct ratio before self.update_steps = int(max(1, self.step / self.steps_per_update)) self.reward_signal_update_steps = int( max(1, self.step / self.reward_signal_steps_per_update) ) 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