import logging from typing import Any, Dict, List, Optional import abc import numpy as np from mlagents.tf_utils import tf from mlagents import tf_utils from mlagents_envs.exception import UnityException from mlagents.trainers.policy import Policy from mlagents.trainers.action_info import ActionInfo from mlagents.trainers.trajectory import SplitObservations from mlagents.trainers.brain_conversion_utils import get_global_agent_id from mlagents_envs.base_env import BatchedStepResult from mlagents.trainers.models import ModelUtils logger = logging.getLogger("mlagents.trainers") class UnityPolicyException(UnityException): """ Related to errors with the Trainer. """ pass class TFPolicy(Policy): """ Contains a learning model, and the necessary functions to save/load models and create the input placeholders. """ def __init__(self, seed, brain, trainer_parameters, load=False): """ 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._version_number_ = 2 self.m_size = 0 # for ghost trainer save/load snapshots self.assign_phs = [] self.assign_ops = [] self.inference_dict = {} self.update_dict = {} self.sequence_length = 1 self.seed = seed self.brain = brain self.act_size = brain.vector_action_space_size self.vec_obs_size = brain.vector_observation_space_size self.vis_obs_size = brain.number_visual_observations self.use_recurrent = trainer_parameters["use_recurrent"] self.memory_dict: Dict[str, np.ndarray] = {} self.reward_signals: Dict[str, "RewardSignal"] = {} self.num_branches = len(self.brain.vector_action_space_size) self.previous_action_dict: Dict[str, np.array] = {} self.normalize = trainer_parameters.get("normalize", False) self.use_continuous_act = brain.vector_action_space_type == "continuous" if self.use_continuous_act: self.num_branches = self.brain.vector_action_space_size[0] self.model_path = trainer_parameters["model_path"] self.keep_checkpoints = trainer_parameters.get("keep_checkpoints", 5) self.graph = tf.Graph() self.sess = tf.Session( config=tf_utils.generate_session_config(), graph=self.graph ) self.saver = None self.seed = seed 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 % 2 != 0: raise UnityPolicyException( "The memory size for brain {0} is {1} " "but it must be divisible by 2.".format( brain.brain_name, self.m_size ) ) self._initialize_tensorflow_references() self.load = load @abc.abstractmethod def get_trainable_variables(self) -> List[tf.Variable]: """ Returns a List of the trainable variables in this policy. if create_tf_graph hasn't been called, returns empty list. """ pass @abc.abstractmethod def create_tf_graph(self): """ Builds the tensorflow graph needed for this policy. """ pass 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 initialize_or_load(self): if self.load: self._load_graph() else: self._initialize_graph() def get_weights(self): with self.graph.as_default(): _vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) values = [v.eval(session=self.sess) for v in _vars] return values def init_load_weights(self): with self.graph.as_default(): _vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) values = [v.eval(session=self.sess) for v in _vars] for var, value in zip(_vars, values): assign_ph = tf.placeholder(var.dtype, shape=value.shape) self.assign_phs.append(assign_ph) self.assign_ops.append(tf.assign(var, assign_ph)) def load_weights(self, values): with self.graph.as_default(): feed_dict = {} for assign_ph, value in zip(self.assign_phs, values): feed_dict[assign_ph] = value self.sess.run(self.assign_ops, feed_dict=feed_dict) def evaluate( self, batched_step_result: BatchedStepResult, global_agent_ids: List[str] ) -> Dict[str, Any]: """ Evaluates policy for the agent experiences provided. :param batched_step_result: BatchedStepResult input to network. :return: Output from policy based on self.inference_dict. """ raise UnityPolicyException("The evaluate function was not implemented.") def get_action( self, batched_step_result: BatchedStepResult, worker_id: int = 0 ) -> ActionInfo: """ Decides actions given observations information, and takes them in environment. :param batched_step_result: A dictionary of brain names and BatchedStepResult from environment. :param worker_id: In parallel environment training, the unique id of the environment worker that the BatchedStepResult came from. Used to construct a globally unique id for each agent. :return: an ActionInfo containing action, memories, values and an object to be passed to add experiences """ if batched_step_result.n_agents() == 0: return ActionInfo.empty() agents_done = [ agent for agent, done in zip( batched_step_result.agent_id, batched_step_result.done ) if done ] self.remove_memories(agents_done) self.remove_previous_action(agents_done) global_agent_ids = [ get_global_agent_id(worker_id, int(agent_id)) for agent_id in batched_step_result.agent_id ] # For 1-D array, the iterator order is correct. run_out = self.evaluate( # pylint: disable=assignment-from-no-return batched_step_result, global_agent_ids ) self.save_memories(global_agent_ids, run_out.get("memory_out")) return ActionInfo( action=run_out.get("action"), value=run_out.get("value"), outputs=run_out, agent_ids=batched_step_result.agent_id, ) 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, batched_step_result): vec_vis_obs = SplitObservations.from_observations(batched_step_result.obs) for i, _ in enumerate(vec_vis_obs.visual_observations): feed_dict[self.visual_in[i]] = vec_vis_obs.visual_observations[i] if self.use_vec_obs: feed_dict[self.vector_in] = vec_vis_obs.vector_observations if not self.use_continuous_act: mask = np.ones( ( batched_step_result.n_agents(), np.sum(self.brain.vector_action_space_size), ), dtype=np.float32, ) if batched_step_result.action_mask is not None: mask = 1 - np.concatenate(batched_step_result.action_mask, axis=1) feed_dict[self.action_masks] = mask 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), dtype=np.float32) def save_memories( self, agent_ids: List[str], memory_matrix: Optional[np.ndarray] ) -> None: if memory_matrix is None: return for index, agent_id in enumerate(agent_ids): self.memory_dict[agent_id] = memory_matrix[index, :] def retrieve_memories(self, agent_ids: List[str]) -> np.ndarray: memory_matrix = np.zeros((len(agent_ids), self.m_size), dtype=np.float32) for index, agent_id in enumerate(agent_ids): if agent_id in self.memory_dict: memory_matrix[index, :] = self.memory_dict[agent_id] return memory_matrix def remove_memories(self, agent_ids): for agent_id in agent_ids: if agent_id in self.memory_dict: self.memory_dict.pop(agent_id) def make_empty_previous_action(self, num_agents): """ Creates empty previous action for use with RNNs and discrete control :param num_agents: Number of agents. :return: Numpy array of zeros. """ return np.zeros((num_agents, self.num_branches), dtype=np.int) def save_previous_action( self, agent_ids: List[str], action_matrix: Optional[np.ndarray] ) -> None: if action_matrix is None: return for index, agent_id in enumerate(agent_ids): self.previous_action_dict[agent_id] = action_matrix[index, :] def retrieve_previous_action(self, agent_ids: List[str]) -> np.ndarray: action_matrix = np.zeros((len(agent_ids), self.num_branches), dtype=np.int) for index, agent_id in enumerate(agent_ids): if agent_id in self.previous_action_dict: action_matrix[index, :] = self.previous_action_dict[agent_id] return action_matrix def remove_previous_action(self, agent_ids): for agent_id in agent_ids: if agent_id in self.previous_action_dict: self.previous_action_dict.pop(agent_id) def get_current_step(self): """ Gets current model step. :return: current model step. """ step = self.sess.run(self.global_step) return step def increment_step(self, n_steps): """ Increments model step. """ out_dict = { "global_step": self.global_step, "increment_step": self.increment_step_op, } feed_dict = {self.steps_to_increment: n_steps} return self.sess.run(out_dict, feed_dict=feed_dict)["global_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) + ".ckpt" self.saver.save(self.sess, last_checkpoint) tf.train.write_graph( self.graph, self.model_path, "raw_graph_def.pb", as_text=False ) def update_normalization(self, vector_obs: np.ndarray) -> None: """ If this policy normalizes vector observations, this will update the norm values in the graph. :param vector_obs: The vector observations to add to the running estimate of the distribution. """ if self.use_vec_obs and self.normalize: self.sess.run( self.update_normalization_op, feed_dict={self.vector_in: vector_obs} ) @property def use_vis_obs(self): return self.vis_obs_size > 0 @property def use_vec_obs(self): return self.vec_obs_size > 0 def _initialize_tensorflow_references(self): self.value_heads: Dict[str, tf.Tensor] = {} self.normalization_steps: Optional[tf.Variable] = None self.running_mean: Optional[tf.Variable] = None self.running_variance: Optional[tf.Variable] = None self.update_normalization_op: Optional[tf.Operation] = None self.value: Optional[tf.Tensor] = None self.all_log_probs: tf.Tensor = None self.log_probs: Optional[tf.Tensor] = None self.entropy: Optional[tf.Tensor] = None self.action_oh: tf.Tensor = None self.output_pre: Optional[tf.Tensor] = None self.output: Optional[tf.Tensor] = None self.selected_actions: Optional[tf.Tensor] = None self.action_holder: Optional[tf.Tensor] = None self.action_masks: Optional[tf.Tensor] = None self.prev_action: Optional[tf.Tensor] = None self.memory_in: Optional[tf.Tensor] = None self.memory_out: Optional[tf.Tensor] = None def create_input_placeholders(self): with self.graph.as_default(): self.global_step, self.increment_step_op, self.steps_to_increment = ( ModelUtils.create_global_steps() ) self.visual_in = ModelUtils.create_visual_input_placeholders( self.brain.camera_resolutions ) self.vector_in = ModelUtils.create_vector_input(self.vec_obs_size) if self.normalize: normalization_tensors = ModelUtils.create_normalizer(self.vector_in) self.update_normalization_op = normalization_tensors.update_op self.normalization_steps = normalization_tensors.steps self.running_mean = normalization_tensors.running_mean self.running_variance = normalization_tensors.running_variance self.processed_vector_in = ModelUtils.normalize_vector_obs( self.vector_in, self.running_mean, self.running_variance, self.normalization_steps, ) else: self.processed_vector_in = self.vector_in self.update_normalization_op = None self.batch_size_ph = tf.placeholder( shape=None, dtype=tf.int32, name="batch_size" ) self.sequence_length_ph = tf.placeholder( shape=None, dtype=tf.int32, name="sequence_length" ) self.mask_input = tf.placeholder( shape=[None], dtype=tf.float32, name="masks" ) # Only needed for PPO, but needed for BC module self.epsilon = tf.placeholder( shape=[None, self.act_size[0]], dtype=tf.float32, name="epsilon" ) self.mask = tf.cast(self.mask_input, tf.int32) tf.Variable( int(self.brain.vector_action_space_type == "continuous"), name="is_continuous_control", trainable=False, dtype=tf.int32, ) tf.Variable( self._version_number_, name="version_number", trainable=False, dtype=tf.int32, ) tf.Variable( self.m_size, name="memory_size", trainable=False, dtype=tf.int32 ) if self.brain.vector_action_space_type == "continuous": tf.Variable( self.act_size[0], name="action_output_shape", trainable=False, dtype=tf.int32, ) else: tf.Variable( sum(self.act_size), name="action_output_shape", trainable=False, dtype=tf.int32, )