import logging import numpy as np from typing import Any, Dict import tensorflow as tf from mlagents.envs.timers import timed from mlagents.envs.brain import BrainInfo from mlagents.trainers.models import EncoderType, LearningRateSchedule from mlagents.trainers.ppo.models import PPOModel from mlagents.trainers.tf_policy import TFPolicy from mlagents.trainers.components.reward_signals.reward_signal_factory import ( create_reward_signal, ) from mlagents.trainers.components.bc.module import BCModule logger = logging.getLogger("mlagents.trainers") class PPOPolicy(TFPolicy): def __init__(self, seed, brain, trainer_params, is_training, load): """ Policy for Proximal Policy Optimization Networks. :param seed: Random seed. :param brain: Assigned Brain object. :param trainer_params: Defined training parameters. :param is_training: Whether the model should be trained. :param load: Whether a pre-trained model will be loaded or a new one created. """ super().__init__(seed, brain, trainer_params) reward_signal_configs = trainer_params["reward_signals"] self.inference_dict = {} self.update_dict = {} self.stats_name_to_update_name = { "Losses/Value Loss": "value_loss", "Losses/Policy Loss": "policy_loss", } self.create_model( brain, trainer_params, reward_signal_configs, is_training, load, seed ) self.create_reward_signals(reward_signal_configs) with self.graph.as_default(): # Create pretrainer if needed if "pretraining" in trainer_params: BCModule.check_config(trainer_params["pretraining"]) self.bc_module = BCModule( self, policy_learning_rate=trainer_params["learning_rate"], default_batch_size=trainer_params["batch_size"], default_num_epoch=trainer_params["num_epoch"], **trainer_params["pretraining"], ) else: self.bc_module = None if load: self._load_graph() else: self._initialize_graph() def create_model( self, brain, trainer_params, reward_signal_configs, is_training, load, seed ): """ Create PPO model :param brain: Assigned Brain object. :param trainer_params: Defined training parameters. :param reward_signal_configs: Reward signal config :param seed: Random seed. """ with self.graph.as_default(): self.model = PPOModel( brain=brain, lr=float(trainer_params["learning_rate"]), lr_schedule=LearningRateSchedule( trainer_params.get("learning_rate_schedule", "linear") ), h_size=int(trainer_params["hidden_units"]), epsilon=float(trainer_params["epsilon"]), beta=float(trainer_params["beta"]), max_step=float(trainer_params["max_steps"]), normalize=trainer_params["normalize"], use_recurrent=trainer_params["use_recurrent"], num_layers=int(trainer_params["num_layers"]), m_size=self.m_size, seed=seed, stream_names=list(reward_signal_configs.keys()), vis_encode_type=EncoderType( trainer_params.get("vis_encode_type", "simple") ), ) self.model.create_ppo_optimizer() self.inference_dict.update( { "action": self.model.output, "log_probs": self.model.all_log_probs, "value_heads": self.model.value_heads, "value": self.model.value, "entropy": self.model.entropy, "learning_rate": self.model.learning_rate, } ) if self.use_continuous_act: self.inference_dict["pre_action"] = self.model.output_pre if self.use_recurrent: self.inference_dict["memory_out"] = self.model.memory_out self.total_policy_loss = self.model.abs_policy_loss self.update_dict.update( { "value_loss": self.model.value_loss, "policy_loss": self.total_policy_loss, "update_batch": self.model.update_batch, } ) def create_reward_signals(self, reward_signal_configs): """ Create reward signals :param reward_signal_configs: Reward signal config. """ self.reward_signals = {} with self.graph.as_default(): # Create reward signals for reward_signal, config in reward_signal_configs.items(): self.reward_signals[reward_signal] = create_reward_signal( self, self.model, reward_signal, config ) self.update_dict.update(self.reward_signals[reward_signal].update_dict) @timed def evaluate(self, brain_info): """ Evaluates policy for the agent experiences provided. :param brain_info: BrainInfo object containing inputs. :return: Outputs from network as defined by self.inference_dict. """ feed_dict = { self.model.batch_size: len(brain_info.vector_observations), self.model.sequence_length: 1, } epsilon = None if self.use_recurrent: if not self.use_continuous_act: feed_dict[ self.model.prev_action ] = brain_info.previous_vector_actions.reshape( [-1, len(self.model.act_size)] ) if brain_info.memories.shape[1] == 0: brain_info.memories = self.make_empty_memory(len(brain_info.agents)) feed_dict[self.model.memory_in] = brain_info.memories if self.use_continuous_act: epsilon = np.random.normal( size=(len(brain_info.vector_observations), self.model.act_size[0]) ) feed_dict[self.model.epsilon] = epsilon feed_dict = self.fill_eval_dict(feed_dict, brain_info) run_out = self._execute_model(feed_dict, self.inference_dict) if self.use_continuous_act: run_out["random_normal_epsilon"] = epsilon return run_out @timed def update(self, mini_batch, num_sequences): """ Performs update on model. :param mini_batch: Batch of experiences. :param num_sequences: Number of sequences to process. :return: Results of update. """ feed_dict = self.construct_feed_dict(self.model, mini_batch, num_sequences) stats_needed = self.stats_name_to_update_name update_stats = {} # Collect feed dicts for all reward signals. for _, reward_signal in self.reward_signals.items(): feed_dict.update( reward_signal.prepare_update(self.model, mini_batch, num_sequences) ) stats_needed.update(reward_signal.stats_name_to_update_name) update_vals = self._execute_model(feed_dict, self.update_dict) for stat_name, update_name in stats_needed.items(): update_stats[stat_name] = update_vals[update_name] return update_stats def construct_feed_dict(self, model, mini_batch, num_sequences): feed_dict = { model.batch_size: num_sequences, model.sequence_length: self.sequence_length, model.mask_input: mini_batch["masks"], model.advantage: mini_batch["advantages"], model.all_old_log_probs: mini_batch["action_probs"], } for name in self.reward_signals: feed_dict[model.returns_holders[name]] = mini_batch[ "{}_returns".format(name) ] feed_dict[model.old_values[name]] = mini_batch[ "{}_value_estimates".format(name) ] if self.use_continuous_act: feed_dict[model.output_pre] = mini_batch["actions_pre"] feed_dict[model.epsilon] = mini_batch["random_normal_epsilon"] else: feed_dict[model.action_holder] = mini_batch["actions"] if self.use_recurrent: feed_dict[model.prev_action] = mini_batch["prev_action"] feed_dict[model.action_masks] = mini_batch["action_mask"] if self.use_vec_obs: feed_dict[model.vector_in] = mini_batch["vector_obs"] if self.model.vis_obs_size > 0: for i, _ in enumerate(self.model.visual_in): feed_dict[model.visual_in[i]] = mini_batch["visual_obs%d" % i] if self.use_recurrent: mem_in = [ mini_batch["memory"][i] for i in range(0, len(mini_batch["memory"]), self.sequence_length) ] feed_dict[model.memory_in] = mem_in return feed_dict def get_value_estimates( self, brain_info: BrainInfo, idx: int, done: bool ) -> Dict[str, float]: """ Generates value estimates for bootstrapping. :param brain_info: BrainInfo to be used for bootstrapping. :param idx: Index in BrainInfo of agent. :param done: Whether or not this is the last element of the episode, in which case the value estimate will be 0. :return: The value estimate dictionary with key being the name of the reward signal and the value the corresponding value estimate. """ feed_dict: Dict[tf.Tensor, Any] = { self.model.batch_size: 1, self.model.sequence_length: 1, } for i in range(len(brain_info.visual_observations)): feed_dict[self.model.visual_in[i]] = [ brain_info.visual_observations[i][idx] ] if self.use_vec_obs: feed_dict[self.model.vector_in] = [brain_info.vector_observations[idx]] if self.use_recurrent: if brain_info.memories.shape[1] == 0: brain_info.memories = self.make_empty_memory(len(brain_info.agents)) feed_dict[self.model.memory_in] = [brain_info.memories[idx]] if not self.use_continuous_act and self.use_recurrent: feed_dict[self.model.prev_action] = [ brain_info.previous_vector_actions[idx] ] value_estimates = self.sess.run(self.model.value_heads, feed_dict) value_estimates = {k: float(v) for k, v in value_estimates.items()} # If we're done, reassign all of the value estimates that need terminal states. if done: for k in value_estimates: if self.reward_signals[k].use_terminal_states: value_estimates[k] = 0.0 return value_estimates