# # Unity ML-Agents Toolkit # ## ML-Agent Learning (PPO) # Contains an implementation of PPO as described in: https://arxiv.org/abs/1707.06347 from collections import defaultdict from typing import cast import numpy as np from mlagents_envs.logging_util import get_logger from mlagents_envs.base_env import BehaviorSpec from mlagents.trainers.policy.nn_policy import NNPolicy from mlagents.trainers.trainer.rl_trainer import RLTrainer from mlagents.trainers.policy.tf_policy import TFPolicy from mlagents.trainers.ppo.optimizer import PPOOptimizer from mlagents.trainers.trajectory import Trajectory from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers from mlagents.trainers.settings import TrainerSettings, PPOSettings logger = get_logger(__name__) class PPOTrainer(RLTrainer): """The PPOTrainer is an implementation of the PPO algorithm.""" 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 PPO 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(PPOTrainer, self).__init__( brain_name, trainer_settings, training, artifact_path, reward_buff_cap ) self.hyperparameters: PPOSettings = cast( PPOSettings, self.trainer_settings.hyperparameters ) self.load = load self.seed = seed self.policy: NNPolicy = None # type: ignore def _process_trajectory(self, trajectory: Trajectory) -> None: """ Takes a trajectory and processes it, putting it into the update buffer. Processing involves calculating value and advantage targets for model updating step. :param trajectory: The Trajectory tuple containing the steps to be processed. """ super()._process_trajectory(trajectory) 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"]) # Get all value estimates value_estimates, value_next = self.optimizer.get_trajectory_value_estimates( agent_buffer_trajectory, trajectory.next_obs, trajectory.done_reached and not trajectory.interrupted, ) for name, v in value_estimates.items(): agent_buffer_trajectory["{}_value_estimates".format(name)].extend(v) self._stats_reporter.add_stat( self.optimizer.reward_signals[name].value_name, np.mean(v) ) # Evaluate all reward functions 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 agent_buffer_trajectory["{}_rewards".format(name)].extend(evaluate_result) # Report the reward signals self.collected_rewards[name][agent_id] += np.sum(evaluate_result) # Compute GAE and returns tmp_advantages = [] tmp_returns = [] for name in self.optimizer.reward_signals: bootstrap_value = value_next[name] local_rewards = agent_buffer_trajectory[ "{}_rewards".format(name) ].get_batch() local_value_estimates = agent_buffer_trajectory[ "{}_value_estimates".format(name) ].get_batch() local_advantage = get_gae( rewards=local_rewards, value_estimates=local_value_estimates, value_next=bootstrap_value, gamma=self.optimizer.reward_signals[name].gamma, lambd=self.hyperparameters.lambd, ) local_return = local_advantage + local_value_estimates # This is later use as target for the different value estimates agent_buffer_trajectory["{}_returns".format(name)].set(local_return) agent_buffer_trajectory["{}_advantage".format(name)].set(local_advantage) tmp_advantages.append(local_advantage) tmp_returns.append(local_return) # Get global advantages global_advantages = list( np.mean(np.array(tmp_advantages, dtype=np.float32), axis=0) ) global_returns = list(np.mean(np.array(tmp_returns, dtype=np.float32), axis=0)) agent_buffer_trajectory["advantages"].set(global_advantages) agent_buffer_trajectory["discounted_returns"].set(global_returns) # Append to update buffer agent_buffer_trajectory.resequence_and_append( self.update_buffer, training_length=self.policy.sequence_length ) # If this was a terminal trajectory, append stats and reset reward collection if trajectory.done_reached: self._update_end_episode_stats(agent_id, self.optimizer) def _is_ready_update(self): """ 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 """ size_of_buffer = self.update_buffer.num_experiences return size_of_buffer > self.hyperparameters.buffer_size def _update_policy(self): """ Uses demonstration_buffer to update the policy. The reward signal generators must be updated in this method at their own pace. """ buffer_length = self.update_buffer.num_experiences self.cumulative_returns_since_policy_update.clear() # Make sure batch_size is a multiple of sequence length. During training, we # will need to reshape the data into a batch_size x sequence_length tensor. batch_size = ( self.hyperparameters.batch_size - self.hyperparameters.batch_size % self.policy.sequence_length ) # Make sure there is at least one sequence batch_size = max(batch_size, self.policy.sequence_length) n_sequences = max( int(self.hyperparameters.batch_size / self.policy.sequence_length), 1 ) advantages = self.update_buffer["advantages"].get_batch() self.update_buffer["advantages"].set( (advantages - advantages.mean()) / (advantages.std() + 1e-10) ) num_epoch = self.hyperparameters.num_epoch batch_update_stats = defaultdict(list) for _ in range(num_epoch): self.update_buffer.shuffle(sequence_length=self.policy.sequence_length) buffer = self.update_buffer max_num_batch = buffer_length // batch_size for i in range(0, max_num_batch * batch_size, batch_size): update_stats = self.optimizer.update( buffer.make_mini_batch(i, i + batch_size), 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)) 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) self._clear_update_buffer() return True def create_policy( self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec ) -> TFPolicy: """ Creates a PPO policy to trainers list of policies. :param behavior_spec: specifications for policy construction :return policy """ policy = NNPolicy( self.seed, behavior_spec, self.trainer_settings, self.is_training, self.artifact_path, self.load, condition_sigma_on_obs=False, # Faster training for PPO create_tf_graph=False, # We will create the TF graph in the Optimizer ) return policy def add_policy( self, parsed_behavior_id: BehaviorIdentifiers, policy: TFPolicy ) -> None: """ Adds policy to trainer. :param parsed_behavior_id: Behavior identifiers that the policy should belong to. :param policy: Policy to associate with name_behavior_id. """ 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-NNPolicy passed to PPOTrainer.add_policy()") self.policy = policy self.optimizer = PPOOptimizer(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() 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 def discount_rewards(r, gamma=0.99, value_next=0.0): """ Computes discounted sum of future rewards for use in updating value estimate. :param r: List of rewards. :param gamma: Discount factor. :param value_next: T+1 value estimate for returns calculation. :return: discounted sum of future rewards as list. """ discounted_r = np.zeros_like(r) running_add = value_next for t in reversed(range(0, r.size)): running_add = running_add * gamma + r[t] discounted_r[t] = running_add return discounted_r def get_gae(rewards, value_estimates, value_next=0.0, gamma=0.99, lambd=0.95): """ Computes generalized advantage estimate for use in updating policy. :param rewards: list of rewards for time-steps t to T. :param value_next: Value estimate for time-step T+1. :param value_estimates: list of value estimates for time-steps t to T. :param gamma: Discount factor. :param lambd: GAE weighing factor. :return: list of advantage estimates for time-steps t to T. """ value_estimates = np.append(value_estimates, value_next) delta_t = rewards + gamma * value_estimates[1:] - value_estimates[:-1] advantage = discount_rewards(r=delta_t, gamma=gamma * lambd) return advantage