from typing import Any, Dict, List, Optional, Tuple import numpy as np from distutils.version import LooseVersion from mlagents_envs.timers import timed from mlagents.model_serialization import SerializationSettings, export_policy_model from mlagents.tf_utils import tf from mlagents import tf_utils from mlagents_envs.exception import UnityException from mlagents_envs.base_env import BehaviorSpec 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.behavior_id_utils import get_global_agent_id from mlagents_envs.base_env import DecisionSteps from mlagents.trainers.tf.models import ModelUtils from mlagents.trainers.settings import TrainerSettings, EncoderType from mlagents.trainers import __version__ from mlagents.trainers.tf.distributions import ( GaussianDistribution, MultiCategoricalDistribution, ) 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 EPSILON = 1e-6 # Small value to avoid divide by zero 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, behavior_spec: BehaviorSpec, trainer_settings: TrainerSettings, model_path: str, load: bool = False, tanh_squash: bool = False, reparameterize: bool = False, condition_sigma_on_obs: bool = True, create_tf_graph: bool = True, ): """ 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. :param model_path: Where to load/save the model. :param load: If True, load model from model_path. Otherwise, create new model. """ super().__init__( seed, behavior_spec, trainer_settings, model_path, load, tanh_squash, reparameterize, condition_sigma_on_obs, ) # for ghost trainer save/load snapshots self.assign_phs: List[tf.Tensor] = [] self.assign_ops: List[tf.Operation] = [] self.update_dict: Dict[str, tf.Tensor] = {} self.inference_dict: Dict[str, tf.Tensor] = {} self.graph = tf.Graph() self.sess = tf.Session( config=tf_utils.generate_session_config(), graph=self.graph ) self.saver: Optional[tf.Operation] = None self._initialize_tensorflow_references() self.grads = None self.update_batch: Optional[tf.Operation] = None self.trainable_variables: List[tf.Variable] = [] if create_tf_graph: self.create_tf_graph() 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. """ return self.trainable_variables def create_tf_graph(self) -> None: """ Builds the tensorflow graph needed for this policy. """ with self.graph.as_default(): tf.set_random_seed(self.seed) _vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) if len(_vars) > 0: # We assume the first thing created in the graph is the Policy. If # already populated, don't create more tensors. return self.create_input_placeholders() encoded = self._create_encoder( self.visual_in, self.processed_vector_in, self.h_size, self.num_layers, self.vis_encode_type, ) if self.use_continuous_act: self._create_cc_actor( encoded, self.tanh_squash, self.reparameterize, self.condition_sigma_on_obs, ) else: self._create_dc_actor(encoded) self.saliency = tf.reduce_mean( tf.square(tf.gradients(self.output, self.vector_in)), axis=1 ) self.trainable_variables = tf.get_collection( tf.GraphKeys.TRAINABLE_VARIABLES, scope="policy" ) self.trainable_variables += tf.get_collection( tf.GraphKeys.TRAINABLE_VARIABLES, scope="lstm" ) # LSTMs need to be root scope for Barracuda export self.inference_dict = { "action": self.output, "log_probs": self.all_log_probs, "entropy": self.entropy, } if self.use_continuous_act: self.inference_dict["pre_action"] = self.output_pre if self.use_recurrent: self.inference_dict["memory_out"] = self.memory_out # We do an initialize to make the Policy usable out of the box. If an optimizer is needed, # it will re-load the full graph self._initialize_graph() def _create_encoder( self, visual_in: List[tf.Tensor], vector_in: tf.Tensor, h_size: int, num_layers: int, vis_encode_type: EncoderType, ) -> tf.Tensor: """ Creates an encoder for visual and vector observations. :param h_size: Size of hidden linear layers. :param num_layers: Number of hidden linear layers. :param vis_encode_type: Type of visual encoder to use if visual input. :return: The hidden layer (tf.Tensor) after the encoder. """ with tf.variable_scope("policy"): encoded = ModelUtils.create_observation_streams( self.visual_in, self.processed_vector_in, 1, h_size, num_layers, vis_encode_type, )[0] return encoded @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(f"Loading model from {model_path}.") ckpt = tf.train.get_checkpoint_state(model_path) if ckpt is None: raise UnityPolicyException( "The model {} 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 {} 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(f"Resuming training from step {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) @timed 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 object containing inputs. :param global_agent_ids: The global (with worker ID) agent ids of the data in the batched_step_result. :return: Outputs from network as defined by self.inference_dict. """ feed_dict = { self.batch_size_ph: len(decision_requests), self.sequence_length_ph: 1, } if self.use_recurrent: if not self.use_continuous_act: feed_dict[self.prev_action] = self.retrieve_previous_action( global_agent_ids ) feed_dict[self.memory_in] = self.retrieve_memories(global_agent_ids) feed_dict = self.fill_eval_dict(feed_dict, decision_requests) run_out = self._execute_model(feed_dict, self.inference_dict) return run_out 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), sum(self.behavior_spec.discrete_action_branches), ), 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 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 checkpoint(self, checkpoint_path: str, settings: SerializationSettings) -> None: """ Checkpoints the policy on disk. :param checkpoint_path: filepath to write the checkpoint :param settings: SerializationSettings for exporting the model. """ # Save the TF checkpoint and graph definition with self.graph.as_default(): if self.saver: self.saver.save(self.sess, f"{checkpoint_path}.ckpt") tf.train.write_graph( self.graph, self.model_path, "raw_graph_def.pb", as_text=False ) # also save the policy so we have optimized model files for each checkpoint self.save(checkpoint_path, settings) def save(self, output_filepath: str, settings: SerializationSettings) -> None: """ Saves the serialized model, given a path and SerializationSettings This method will save the policy graph to the given filepath. The path should be provided without an extension as multiple serialized model formats may be generated as a result. :param output_filepath: path (without suffix) for the model file(s) :param settings: SerializationSettings for how to save the model. """ export_policy_model(output_filepath, settings, self.graph, self.sess) 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.vector_in, self.visual_in = ModelUtils.create_input_placeholders( self.behavior_spec.observation_shapes ) 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.behavior_spec.is_action_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.behavior_spec.is_action_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, ) def _create_cc_actor( self, encoded: tf.Tensor, tanh_squash: bool = False, reparameterize: bool = False, condition_sigma_on_obs: bool = True, ) -> None: """ Creates Continuous control actor-critic model. :param h_size: Size of hidden linear layers. :param num_layers: Number of hidden linear layers. :param vis_encode_type: Type of visual encoder to use if visual input. :param tanh_squash: Whether to use a tanh function, or a clipped output. :param reparameterize: Whether we are using the resampling trick to update the policy. """ if self.use_recurrent: self.memory_in = tf.placeholder( shape=[None, self.m_size], dtype=tf.float32, name="recurrent_in" ) hidden_policy, memory_policy_out = ModelUtils.create_recurrent_encoder( encoded, self.memory_in, self.sequence_length_ph, name="lstm_policy" ) self.memory_out = tf.identity(memory_policy_out, name="recurrent_out") else: hidden_policy = encoded with tf.variable_scope("policy"): distribution = GaussianDistribution( hidden_policy, self.act_size, reparameterize=reparameterize, tanh_squash=tanh_squash, condition_sigma=condition_sigma_on_obs, ) if tanh_squash: self.output_pre = distribution.sample self.output = tf.identity(self.output_pre, name="action") else: self.output_pre = distribution.sample # Clip and scale output to ensure actions are always within [-1, 1] range. output_post = tf.clip_by_value(self.output_pre, -3, 3) / 3 self.output = tf.identity(output_post, name="action") self.selected_actions = tf.stop_gradient(self.output) self.all_log_probs = tf.identity(distribution.log_probs, name="action_probs") self.entropy = distribution.entropy # We keep these tensors the same name, but use new nodes to keep code parallelism with discrete control. self.total_log_probs = distribution.total_log_probs def _create_dc_actor(self, encoded: tf.Tensor) -> None: """ Creates Discrete control actor-critic model. :param h_size: Size of hidden linear layers. :param num_layers: Number of hidden linear layers. :param vis_encode_type: Type of visual encoder to use if visual input. """ if self.use_recurrent: self.prev_action = tf.placeholder( shape=[None, len(self.act_size)], dtype=tf.int32, name="prev_action" ) prev_action_oh = tf.concat( [ tf.one_hot(self.prev_action[:, i], self.act_size[i]) for i in range(len(self.act_size)) ], axis=1, ) hidden_policy = tf.concat([encoded, prev_action_oh], axis=1) self.memory_in = tf.placeholder( shape=[None, self.m_size], dtype=tf.float32, name="recurrent_in" ) hidden_policy, memory_policy_out = ModelUtils.create_recurrent_encoder( hidden_policy, self.memory_in, self.sequence_length_ph, name="lstm_policy", ) self.memory_out = tf.identity(memory_policy_out, "recurrent_out") else: hidden_policy = encoded self.action_masks = tf.placeholder( shape=[None, sum(self.act_size)], dtype=tf.float32, name="action_masks" ) with tf.variable_scope("policy"): distribution = MultiCategoricalDistribution( hidden_policy, self.act_size, self.action_masks ) # It's important that we are able to feed_dict a value into this tensor to get the # right one-hot encoding, so we can't do identity on it. self.output = distribution.sample self.all_log_probs = tf.identity(distribution.log_probs, name="action") self.selected_actions = tf.stop_gradient( distribution.sample_onehot ) # In discrete, these are onehot self.entropy = distribution.entropy self.total_log_probs = distribution.total_log_probs