# # Unity ML-Agents Toolkit # ## ML-Agent Learning (PPO) # Contains an implementation of PPO as described in: https://arxiv.org/abs/1707.06347 import logging from collections import defaultdict from typing import Dict import numpy as np from mlagents.envs.brain import AllBrainInfo from mlagents.trainers.ppo.policy import PPOPolicy from mlagents.trainers.ppo.multi_gpu_policy import MultiGpuPPOPolicy, get_devices from mlagents.trainers.rl_trainer import RLTrainer, AllRewardsOutput from mlagents.envs.action_info import ActionInfoOutputs logger = logging.getLogger("mlagents.trainers") class PPOTrainer(RLTrainer): """The PPOTrainer is an implementation of the PPO algorithm.""" def __init__( self, brain, reward_buff_cap, trainer_parameters, training, load, seed, run_id, multi_gpu, ): """ Responsible for collecting experiences and training PPO model. :param trainer_parameters: The parameters for the trainer (dictionary). :param reward_buff_cap: Max reward history to track in the reward buffer :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 identifier of the current run """ super(PPOTrainer, self).__init__( brain, trainer_parameters, training, run_id, reward_buff_cap ) self.param_keys = [ "batch_size", "beta", "buffer_size", "epsilon", "hidden_units", "lambd", "learning_rate", "max_steps", "normalize", "num_epoch", "num_layers", "time_horizon", "sequence_length", "summary_freq", "use_recurrent", "summary_path", "memory_size", "model_path", "reward_signals", ] self.check_param_keys() if multi_gpu and len(get_devices()) > 1: self.policy = MultiGpuPPOPolicy( seed, brain, trainer_parameters, self.is_training, load ) else: self.policy = PPOPolicy( seed, brain, trainer_parameters, self.is_training, load ) for _reward_signal in self.policy.reward_signals.keys(): self.collected_rewards[_reward_signal] = {} def process_experiences( self, current_info: AllBrainInfo, new_info: AllBrainInfo ) -> None: """ Checks agent histories for processing condition, and processes them as necessary. Processing involves calculating value and advantage targets for model updating step. :param current_info: Dictionary of all current brains and corresponding BrainInfo. :param new_info: Dictionary of all next brains and corresponding BrainInfo. """ info = new_info[self.brain_name] if self.is_training: self.policy.update_normalization(info.vector_observations) for l in range(len(info.agents)): agent_actions = self.training_buffer[info.agents[l]]["actions"] if ( info.local_done[l] or len(agent_actions) > self.trainer_parameters["time_horizon"] ) and len(agent_actions) > 0: agent_id = info.agents[l] if info.max_reached[l]: bootstrapping_info = self.training_buffer[agent_id].last_brain_info idx = bootstrapping_info.agents.index(agent_id) else: bootstrapping_info = info idx = l value_next = self.policy.get_value_estimates( bootstrapping_info, idx, info.local_done[l] and not info.max_reached[l], ) tmp_advantages = [] tmp_returns = [] for name in self.policy.reward_signals: bootstrap_value = value_next[name] local_rewards = self.training_buffer[agent_id][ "{}_rewards".format(name) ].get_batch() local_value_estimates = self.training_buffer[agent_id][ "{}_value_estimates".format(name) ].get_batch() local_advantage = get_gae( rewards=local_rewards, value_estimates=local_value_estimates, value_next=bootstrap_value, gamma=self.policy.reward_signals[name].gamma, lambd=self.trainer_parameters["lambd"], ) local_return = local_advantage + local_value_estimates # This is later use as target for the different value estimates self.training_buffer[agent_id]["{}_returns".format(name)].set( local_return ) self.training_buffer[agent_id]["{}_advantage".format(name)].set( local_advantage ) tmp_advantages.append(local_advantage) tmp_returns.append(local_return) global_advantages = list(np.mean(np.array(tmp_advantages), axis=0)) global_returns = list(np.mean(np.array(tmp_returns), axis=0)) self.training_buffer[agent_id]["advantages"].set(global_advantages) self.training_buffer[agent_id]["discounted_returns"].set(global_returns) self.training_buffer.append_update_buffer( agent_id, batch_size=None, training_length=self.policy.sequence_length, ) self.training_buffer[agent_id].reset_agent() if info.local_done[l]: self.stats["Environment/Episode Length"].append( self.episode_steps.get(agent_id, 0) ) self.episode_steps[agent_id] = 0 for name, rewards in self.collected_rewards.items(): if name == "environment": self.cumulative_returns_since_policy_update.append( rewards.get(agent_id, 0) ) self.stats["Environment/Cumulative Reward"].append( rewards.get(agent_id, 0) ) self.reward_buffer.appendleft(rewards.get(agent_id, 0)) rewards[agent_id] = 0 else: self.stats[ self.policy.reward_signals[name].stat_name ].append(rewards.get(agent_id, 0)) rewards[agent_id] = 0 def add_policy_outputs( self, take_action_outputs: ActionInfoOutputs, agent_id: str, agent_idx: int ) -> None: """ Takes the output of the last action and store it into the training buffer. """ actions = take_action_outputs["action"] if self.policy.use_continuous_act: actions_pre = take_action_outputs["pre_action"] self.training_buffer[agent_id]["actions_pre"].append(actions_pre[agent_idx]) epsilons = take_action_outputs["random_normal_epsilon"] self.training_buffer[agent_id]["random_normal_epsilon"].append( epsilons[agent_idx] ) a_dist = take_action_outputs["log_probs"] # value is a dictionary from name of reward to value estimate of the value head self.training_buffer[agent_id]["actions"].append(actions[agent_idx]) self.training_buffer[agent_id]["action_probs"].append(a_dist[agent_idx]) def add_rewards_outputs( self, rewards_out: AllRewardsOutput, values: Dict[str, np.ndarray], agent_id: str, agent_idx: int, agent_next_idx: int, ) -> None: """ Takes the value output of the last action and store it into the training buffer. """ for name, reward_result in rewards_out.reward_signals.items(): # 0 because we use the scaled reward to train the agent self.training_buffer[agent_id]["{}_rewards".format(name)].append( reward_result.scaled_reward[agent_next_idx] ) self.training_buffer[agent_id]["{}_value_estimates".format(name)].append( values[name][agent_idx][0] ) 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 = len(self.training_buffer.update_buffer["actions"]) return size_of_buffer > self.trainer_parameters["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 = len(self.training_buffer.update_buffer["actions"]) self.trainer_metrics.start_policy_update_timer( number_experiences=buffer_length, mean_return=float(np.mean(self.cumulative_returns_since_policy_update)), ) self.cumulative_returns_since_policy_update = [] # 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.trainer_parameters["batch_size"] - self.trainer_parameters["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.trainer_parameters["batch_size"] / self.policy.sequence_length), 1 ) advantages = self.training_buffer.update_buffer["advantages"].get_batch() self.training_buffer.update_buffer["advantages"].set( (advantages - advantages.mean()) / (advantages.std() + 1e-10) ) num_epoch = self.trainer_parameters["num_epoch"] batch_update_stats = defaultdict(list) for _ in range(num_epoch): self.training_buffer.update_buffer.shuffle( sequence_length=self.policy.sequence_length ) buffer = self.training_buffer.update_buffer max_num_batch = buffer_length // batch_size for l in range(0, max_num_batch * batch_size, batch_size): update_stats = self.policy.update( buffer.make_mini_batch(l, l + 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[stat].append(np.mean(stat_list)) if self.policy.bc_module: update_stats = self.policy.bc_module.update() for stat, val in update_stats.items(): self.stats[stat].append(val) self.training_buffer.reset_update_buffer() self.trainer_metrics.end_policy_update() 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