import logging import numpy as np from mlagents.trainers.ppo.models import PPOModel from mlagents.trainers.policy import Policy logger = logging.getLogger("mlagents.trainers") class PPOPolicy(Policy): 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) self.has_updated = False self.use_curiosity = bool(trainer_params['use_curiosity']) with self.graph.as_default(): self.model = PPOModel(brain, lr=float(trainer_params['learning_rate']), 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, use_curiosity=bool(trainer_params['use_curiosity']), curiosity_strength=float(trainer_params['curiosity_strength']), curiosity_enc_size=float(trainer_params['curiosity_enc_size']), seed=seed) if load: self._load_graph() else: self._initialize_graph() self.inference_dict = {'action': self.model.output, 'log_probs': self.model.all_log_probs, '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 if is_training and self.use_vec_obs and trainer_params['normalize']: self.inference_dict['update_mean'] = self.model.update_mean self.inference_dict['update_variance'] = self.model.update_variance self.update_dict = {'value_loss': self.model.value_loss, 'policy_loss': self.model.policy_loss, 'update_batch': self.model.update_batch} if self.use_curiosity: self.update_dict['forward_loss'] = self.model.forward_loss self.update_dict['inverse_loss'] = self.model.inverse_loss 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 def update(self, mini_batch, num_sequences): """ Updates model using buffer. :param num_sequences: Number of trajectories in batch. :param mini_batch: Experience batch. :return: Output from update process. """ feed_dict = {self.model.batch_size: num_sequences, self.model.sequence_length: self.sequence_length, self.model.mask_input: mini_batch['masks'].flatten(), self.model.returns_holder: mini_batch['discounted_returns'].flatten(), self.model.old_value: mini_batch['value_estimates'].flatten(), self.model.advantage: mini_batch['advantages'].reshape([-1, 1]), self.model.all_old_log_probs: mini_batch['action_probs'].reshape( [-1, sum(self.model.act_size)])} if self.use_continuous_act: feed_dict[self.model.output_pre] = mini_batch['actions_pre'].reshape( [-1, self.model.act_size[0]]) feed_dict[self.model.epsilon] = mini_batch['random_normal_epsilon'].reshape( [-1, self.model.act_size[0]]) else: feed_dict[self.model.action_holder] = mini_batch['actions'].reshape( [-1, len(self.model.act_size)]) if self.use_recurrent: feed_dict[self.model.prev_action] = mini_batch['prev_action'].reshape( [-1, len(self.model.act_size)]) feed_dict[self.model.action_masks] = mini_batch['action_mask'].reshape( [-1, sum(self.brain.vector_action_space_size)]) if self.use_vec_obs: feed_dict[self.model.vector_in] = mini_batch['vector_obs'].reshape( [-1, self.vec_obs_size]) if self.use_curiosity: feed_dict[self.model.next_vector_in] = mini_batch['next_vector_in'].reshape( [-1, self.vec_obs_size]) if self.model.vis_obs_size > 0: for i, _ in enumerate(self.model.visual_in): _obs = mini_batch['visual_obs%d' % i] if self.sequence_length > 1 and self.use_recurrent: (_batch, _seq, _w, _h, _c) = _obs.shape feed_dict[self.model.visual_in[i]] = _obs.reshape([-1, _w, _h, _c]) else: feed_dict[self.model.visual_in[i]] = _obs if self.use_curiosity: for i, _ in enumerate(self.model.visual_in): _obs = mini_batch['next_visual_obs%d' % i] if self.sequence_length > 1 and self.use_recurrent: (_batch, _seq, _w, _h, _c) = _obs.shape feed_dict[self.model.next_visual_in[i]] = _obs.reshape([-1, _w, _h, _c]) else: feed_dict[self.model.next_visual_in[i]] = _obs if self.use_recurrent: mem_in = mini_batch['memory'][:, 0, :] feed_dict[self.model.memory_in] = mem_in self.has_updated = True run_out = self._execute_model(feed_dict, self.update_dict) return run_out def get_intrinsic_rewards(self, curr_info, next_info): """ Generates intrinsic reward used for Curiosity-based training. :BrainInfo curr_info: Current BrainInfo. :BrainInfo next_info: Next BrainInfo. :return: Intrinsic rewards for all agents. """ if self.use_curiosity: if len(curr_info.agents) == 0: return [] feed_dict = {self.model.batch_size: len(next_info.vector_observations), self.model.sequence_length: 1} if self.use_continuous_act: feed_dict[self.model.selected_actions] = next_info.previous_vector_actions else: feed_dict[self.model.action_holder] = next_info.previous_vector_actions for i in range(self.model.vis_obs_size): feed_dict[self.model.visual_in[i]] = curr_info.visual_observations[i] feed_dict[self.model.next_visual_in[i]] = next_info.visual_observations[i] if self.use_vec_obs: feed_dict[self.model.vector_in] = curr_info.vector_observations feed_dict[self.model.next_vector_in] = next_info.vector_observations if self.use_recurrent: if curr_info.memories.shape[1] == 0: curr_info.memories = self.make_empty_memory(len(curr_info.agents)) feed_dict[self.model.memory_in] = curr_info.memories intrinsic_rewards = self.sess.run(self.model.intrinsic_reward, feed_dict=feed_dict) * float(self.has_updated) return intrinsic_rewards else: return None def get_value_estimate(self, brain_info, idx): """ Generates value estimates for bootstrapping. :param brain_info: BrainInfo to be used for bootstrapping. :param idx: Index in BrainInfo of agent. :return: Value estimate. """ feed_dict = {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].reshape( [-1, len(self.model.act_size)]) value_estimate = self.sess.run(self.model.value, feed_dict) return value_estimate def get_last_reward(self): """ Returns the last reward the trainer has had :return: the new last reward """ return self.sess.run(self.model.last_reward) def update_reward(self, new_reward): """ Updates reward value for policy. :param new_reward: New reward to save. """ self.sess.run(self.model.update_reward, feed_dict={self.model.new_reward: new_reward})