ml-agents/ml-agents/mlagents/trainers/policy.py

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',
                             'version_number', 'is_continuous_control',
                             'action_output_shape']

    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 .tf 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