import logging import numpy as np import tensorflow as tf from mlagents.trainers import UnityException from mlagents.trainers.models import LearningModel from tensorflow.python.tools import freeze_graph logger = logging.getLogger("mlagents.trainers") class UnityPolicyException(UnityException): """ Related to errors with the Trainer. """ pass class Policy(object): """ Contains a learning model, and the necessary functions to interact with it to perform evaluate and updating. """ possible_output_nodes = ['action', 'value_estimate', 'action_probs', 'recurrent_out', 'memory_size'] def __init__(self, seed, brain, trainer_parameters): """ Initialized the policy. :param seed: Random seed to use for TensorFlow. :param brain: The corresponding Brain for this policy. :param trainer_parameters: The trainer parameters. """ self.m_size = None self.model = None self.inference_dict = {} self.update_dict = {} self.sequence_length = 1 self.seed = seed self.brain = brain self.use_recurrent = trainer_parameters["use_recurrent"] self.use_continuous_act = (brain.vector_action_space_type == "continuous") self.model_path = trainer_parameters["model_path"] self.keep_checkpoints = trainer_parameters.get("keep_checkpoints", 5) self.graph = tf.Graph() config = tf.ConfigProto() config.gpu_options.allow_growth = True self.sess = tf.Session(config=config, graph=self.graph) self.saver = None if self.use_recurrent: self.m_size = trainer_parameters["memory_size"] self.sequence_length = trainer_parameters["sequence_length"] if self.m_size == 0: raise UnityPolicyException("The memory size for brain {0} is 0 even " "though the trainer uses recurrent." .format(brain.brain_name)) elif self.m_size % 4 != 0: raise UnityPolicyException("The memory size for brain {0} is {1} " "but it must be divisible by 4." .format(brain.brain_name, self.m_size)) def _initialize_graph(self): with self.graph.as_default(): self.saver = tf.train.Saver(max_to_keep=self.keep_checkpoints) init = tf.global_variables_initializer() self.sess.run(init) def _load_graph(self): with self.graph.as_default(): self.saver = tf.train.Saver(max_to_keep=self.keep_checkpoints) logger.info('Loading Model for brain {}'.format(self.brain.brain_name)) ckpt = tf.train.get_checkpoint_state(self.model_path) if ckpt is None: logger.info('The model {0} could not be found. Make ' 'sure you specified the right ' '--run-id' .format(self.model_path)) self.saver.restore(self.sess, ckpt.model_checkpoint_path) def evaluate(self, brain_info): """ Evaluates policy for the agent experiences provided. :param brain_info: BrainInfo input to network. :return: Output from policy based on self.inference_dict. """ raise UnityPolicyException("The evaluate function was not implemented.") def update(self, mini_batch, num_sequences): """ Performs update of the policy. :param num_sequences: Number of experience trajectories in batch. :param mini_batch: Batch of experiences. :return: Results of update. """ raise UnityPolicyException("The update function was not implemented.") def _execute_model(self, feed_dict, out_dict): """ Executes model. :param feed_dict: Input dictionary mapping nodes to input data. :param out_dict: Output dictionary mapping names to nodes. :return: Dictionary mapping names to input data. """ network_out = self.sess.run(list(out_dict.values()), feed_dict=feed_dict) run_out = dict(zip(list(out_dict.keys()), network_out)) return run_out def _fill_eval_dict(self, feed_dict, brain_info): for i, _ in enumerate(brain_info.visual_observations): feed_dict[self.model.visual_in[i]] = brain_info.visual_observations[i] if self.use_vec_obs: feed_dict[self.model.vector_in] = brain_info.vector_observations if not self.use_continuous_act: feed_dict[self.model.action_masks] = brain_info.action_masks return feed_dict def make_empty_memory(self, num_agents): """ Creates empty memory for use with RNNs :param num_agents: Number of agents. :return: Numpy array of zeros. """ return np.zeros((num_agents, self.m_size)) def get_current_step(self): """ Gets current model step. :return: current model step. """ step = self.sess.run(self.model.global_step) return step def increment_step(self): """ Increments model step. """ self.sess.run(self.model.increment_step) def get_inference_vars(self): """ :return:list of inference var names """ return list(self.inference_dict.keys()) def get_update_vars(self): """ :return:list of update var names """ return list(self.update_dict.keys()) def save_model(self, steps): """ Saves the model :param steps: The number of steps the model was trained for :return: """ with self.graph.as_default(): last_checkpoint = self.model_path + '/model-' + str(steps) + '.cptk' self.saver.save(self.sess, last_checkpoint) tf.train.write_graph(self.graph, self.model_path, 'raw_graph_def.pb', as_text=False) def export_model(self): """ Exports latest saved model to .bytes format for Unity embedding. """ with self.graph.as_default(): target_nodes = ','.join(self._process_graph()) ckpt = tf.train.get_checkpoint_state(self.model_path) freeze_graph.freeze_graph( input_graph=self.model_path + '/raw_graph_def.pb', input_binary=True, input_checkpoint=ckpt.model_checkpoint_path, output_node_names=target_nodes, output_graph=(self.model_path + '.bytes'), clear_devices=True, initializer_nodes='', input_saver='', restore_op_name='save/restore_all', filename_tensor_name='save/Const:0') def _process_graph(self): """ Gets the list of the output nodes present in the graph for inference :return: list of node names """ all_nodes = [x.name for x in self.graph.as_graph_def().node] nodes = [x for x in all_nodes if x in self.possible_output_nodes] logger.info('List of nodes to export for brain :' + self.brain.brain_name) for n in nodes: logger.info('\t' + n) return nodes @property def vis_obs_size(self): return self.model.vis_obs_size @property def vec_obs_size(self): return self.model.vec_obs_size @property def use_vis_obs(self): return self.model.vis_obs_size > 0 @property def use_vec_obs(self): return self.model.vec_obs_size > 0