from typing import Any, Dict, List, Optional, Tuple import abc import os import numpy as np from distutils.version import LooseVersion from mlagents.tf_utils import tf from mlagents import tf_utils from mlagents_envs.exception import UnityException from mlagents_envs.logging_util import get_logger 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 DecisionSteps from mlagents.trainers.models import ModelUtils from mlagents.trainers.settings import TrainerSettings, NetworkSettings from mlagents.trainers.brain import BrainParameters from mlagents.trainers import __version__ logger = get_logger(__name__) # This is the version number of the inputs and outputs of the model, and # determines compatibility with inference in Barracuda. MODEL_FORMAT_VERSION = 2 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: int, brain: BrainParameters, trainer_settings: TrainerSettings, load: bool = False, ): """ Initialized the policy. :param seed: Random seed to use for TensorFlow. :param brain: The corresponding Brain for this policy. :param trainer_settings: The trainer parameters. """ self.m_size = 0 self.trainer_settings = trainer_settings self.network_settings: NetworkSettings = trainer_settings.network_settings # for ghost trainer save/load snapshots self.assign_phs: List[tf.Tensor] = [] self.assign_ops: List[tf.Operation] = [] self.inference_dict: Dict[str, tf.Tensor] = {} self.update_dict: Dict[str, tf.Tensor] = {} 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 = self.network_settings.memory is not None self.memory_dict: Dict[str, np.ndarray] = {} self.num_branches = len(self.brain.vector_action_space_size) self.previous_action_dict: Dict[str, np.array] = {} self.normalize = self.network_settings.normalize 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 = self.trainer_settings.output_path self.initialize_path = self.trainer_settings.init_path self.keep_checkpoints = self.trainer_settings.keep_checkpoints self.graph = tf.Graph() self.sess = tf.Session( config=tf_utils.generate_session_config(), graph=self.graph ) self.saver: Optional[tf.Operation] = None self.seed = seed if self.network_settings.memory is not None: self.m_size = self.network_settings.memory.memory_size self.sequence_length = self.network_settings.memory.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 @staticmethod def _convert_version_string(version_string: str) -> Tuple[int, ...]: """ Converts the version string into a Tuple of ints (major_ver, minor_ver, patch_ver). :param version_string: The semantic-versioned version string (X.Y.Z). :return: A Tuple containing (major_ver, minor_ver, patch_ver). """ ver = LooseVersion(version_string) return tuple(map(int, ver.version[0:3])) def _check_model_version(self, version: str) -> None: """ Checks whether the model being loaded was created with the same version of ML-Agents, and throw a warning if not so. """ if self.version_tensors is not None: loaded_ver = tuple( num.eval(session=self.sess) for num in self.version_tensors ) if loaded_ver != TFPolicy._convert_version_string(version): logger.warning( f"The model checkpoint you are loading from was saved with ML-Agents version " f"{loaded_ver[0]}.{loaded_ver[1]}.{loaded_ver[2]} but your current ML-Agents" f"version is {version}. Model may not behave properly." ) 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, model_path: str, reset_global_steps: bool = False) -> None: with self.graph.as_default(): self.saver = tf.train.Saver(max_to_keep=self.keep_checkpoints) logger.info( "Loading model for brain {} from {}.".format( self.brain.brain_name, model_path ) ) ckpt = tf.train.get_checkpoint_state(model_path) if ckpt is None: raise UnityPolicyException( "The model {0} could not be loaded. Make " "sure you specified the right " "--run-id and that the previous run you are loading from had the same " "behavior names.".format(model_path) ) try: self.saver.restore(self.sess, ckpt.model_checkpoint_path) except tf.errors.NotFoundError: raise UnityPolicyException( "The model {0} was found but could not be loaded. Make " "sure the model is from the same version of ML-Agents, has the same behavior parameters, " "and is using the same trainer configuration as the current run.".format( model_path ) ) self._check_model_version(__version__) if reset_global_steps: self._set_step(0) logger.info( "Starting training from step 0 and saving to {}.".format( self.model_path ) ) else: logger.info( "Resuming training from step {}.".format(self.get_current_step()) ) def initialize_or_load(self): # If there is an initialize path, load from that. Else, load from the set model path. # If load is set to True, don't reset steps to 0. Else, do. This allows a user to, # e.g., resume from an initialize path. reset_steps = not self.load if self.initialize_path is not None: self._load_graph(self.initialize_path, reset_global_steps=reset_steps) elif self.load: self._load_graph(self.model_path, reset_global_steps=reset_steps) 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): if len(self.assign_ops) == 0: logger.warning( "Calling load_weights in tf_policy but assign_ops is empty. Did you forget to call init_load_weights?" ) 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, decision_requests: DecisionSteps, global_agent_ids: List[str] ) -> Dict[str, Any]: """ Evaluates policy for the agent experiences provided. :param decision_requests: DecisionSteps input to network. :return: Output from policy based on self.inference_dict. """ raise UnityPolicyException("The evaluate function was not implemented.") def get_action( self, decision_requests: DecisionSteps, worker_id: int = 0 ) -> ActionInfo: """ Decides actions given observations information, and takes them in environment. :param decision_requests: A dictionary of brain names and DecisionSteps from environment. :param worker_id: In parallel environment training, the unique id of the environment worker that the DecisionSteps 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 len(decision_requests) == 0: return ActionInfo.empty() global_agent_ids = [ get_global_agent_id(worker_id, int(agent_id)) for agent_id in decision_requests.agent_id ] # For 1-D array, the iterator order is correct. run_out = self.evaluate( # pylint: disable=assignment-from-no-return decision_requests, 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=decision_requests.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( (len(batched_step_result), 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 _set_step(self, step: int) -> int: """ Sets current model step to step without creating additional ops. :param step: Step to set the current model step to. :return: The step the model was set to. """ current_step = self.get_current_step() # Increment a positive or negative number of steps. return self.increment_step(step - current_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 = os.path.join(self.model_path, f"model-{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.total_log_probs: Optional[tf.Tensor] = None self.entropy: Optional[tf.Tensor] = None self.output_pre: Optional[tf.Tensor] = None self.output: Optional[tf.Tensor] = None self.selected_actions: 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 self.version_tensors: Optional[Tuple[tf.Tensor, tf.Tensor, 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, ) int_version = TFPolicy._convert_version_string(__version__) major_ver_t = tf.Variable( int_version[0], name="trainer_major_version", trainable=False, dtype=tf.int32, ) minor_ver_t = tf.Variable( int_version[1], name="trainer_minor_version", trainable=False, dtype=tf.int32, ) patch_ver_t = tf.Variable( int_version[2], name="trainer_patch_version", trainable=False, dtype=tf.int32, ) self.version_tensors = (major_ver_t, minor_ver_t, patch_ver_t) tf.Variable( MODEL_FORMAT_VERSION, 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, )