Source code for ray.rllib.policy.tf_policy

import errno
import logging
import os

import numpy as np
import ray
import ray.experimental.tf_utils
from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY, \
from ray.rllib.policy.rnn_sequencing import chop_into_sequences
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils.debug import log_once, summarize
from ray.rllib.utils.exploration.exploration import Exploration
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.schedules import ConstantSchedule, PiecewiseSchedule
from ray.rllib.utils.tf_run_builder import TFRunBuilder

tf = try_import_tf()
logger = logging.getLogger(__name__)

[docs]@DeveloperAPI class TFPolicy(Policy): """An agent policy and loss implemented in TensorFlow. Extending this class enables RLlib to perform TensorFlow specific optimizations on the policy, e.g., parallelization across gpus or fusing multiple graphs together in the multi-agent setting. Input tensors are typically shaped like [BATCH_SIZE, ...]. Attributes: observation_space (gym.Space): observation space of the policy. action_space (gym.Space): action space of the policy. model (rllib.models.Model): RLlib model used for the policy. Examples: >>> policy = TFPolicySubclass( sess, obs_input, sampled_action, loss, loss_inputs) >>> print(policy.compute_actions([1, 0, 2])) (array([0, 1, 1]), [], {}) >>> print(policy.postprocess_trajectory(SampleBatch({...}))) SampleBatch({"action": ..., "advantages": ..., ...}) """ @DeveloperAPI def __init__(self, observation_space, action_space, config, sess, obs_input, sampled_action, loss, loss_inputs, model=None, sampled_action_logp=None, action_input=None, log_likelihood=None, state_inputs=None, state_outputs=None, prev_action_input=None, prev_reward_input=None, seq_lens=None, max_seq_len=20, batch_divisibility_req=1, update_ops=None, explore=None, timestep=None): """Initialize the policy. Arguments: observation_space (gym.Space): Observation space of the env. action_space (gym.Space): Action space of the env. config (dict): The Policy config dict. sess (Session): The TensorFlow session to use. obs_input (Tensor): Input placeholder for observations, of shape [BATCH_SIZE, obs...]. sampled_action (Tensor): Tensor for sampling an action, of shape [BATCH_SIZE, action...] loss (Tensor): Scalar policy loss output tensor. loss_inputs (list): A (name, placeholder) tuple for each loss input argument. Each placeholder name must correspond to a SampleBatch column key returned by postprocess_trajectory(), and has shape [BATCH_SIZE, data...]. These keys will be read from postprocessed sample batches and fed into the specified placeholders during loss computation. model (rllib.models.Model): used to integrate custom losses and stats from user-defined RLlib models. sampled_action_logp (Tensor): log probability of the sampled action. action_input (Optional[Tensor]): Input placeholder for actions for logp/log-likelihood calculations. log_likelihood (Optional[Tensor]): Tensor to calculate the log_likelihood (given action_input and obs_input). state_inputs (list): list of RNN state input Tensors. state_outputs (list): list of RNN state output Tensors. prev_action_input (Tensor): placeholder for previous actions prev_reward_input (Tensor): placeholder for previous rewards seq_lens (Tensor): Placeholder for RNN sequence lengths, of shape [NUM_SEQUENCES]. Note that NUM_SEQUENCES << BATCH_SIZE. See policy/ for more information. max_seq_len (int): Max sequence length for LSTM training. batch_divisibility_req (int): pad all agent experiences batches to multiples of this value. This only has an effect if not using a LSTM model. update_ops (list): override the batchnorm update ops to run when applying gradients. Otherwise we run all update ops found in the current variable scope. explore (Tensor): Placeholder for `explore` parameter into call to Exploration.get_exploration_action. timestep (Tensor): Placeholder for the global sampling timestep. """ self.framework = "tf" super().__init__(observation_space, action_space, config) self.model = model self._sess = sess self._obs_input = obs_input self._prev_action_input = prev_action_input self._prev_reward_input = prev_reward_input self._sampled_action = sampled_action self._is_training = self._get_is_training_placeholder() self._is_exploring = explore if explore is not None else \ tf.placeholder_with_default(True, (), name="is_exploring") self._sampled_action_logp = sampled_action_logp self._sampled_action_prob = (tf.exp(self._sampled_action_logp) if self._sampled_action_logp is not None else None) self._action_input = action_input # For logp calculations. self._log_likelihood = log_likelihood self._state_inputs = state_inputs or [] self._state_outputs = state_outputs or [] self._seq_lens = seq_lens self._max_seq_len = max_seq_len self._batch_divisibility_req = batch_divisibility_req self._update_ops = update_ops self._stats_fetches = {} self._loss_input_dict = None self.exploration_info = self.exploration.get_info() self._timestep = timestep if timestep is not None else \ tf.placeholder(tf.int32, (), name="timestep") if loss is not None: self._initialize_loss(loss, loss_inputs) else: self._loss = None if len(self._state_inputs) != len(self._state_outputs): raise ValueError( "Number of state input and output tensors must match, got: " "{} vs {}".format(self._state_inputs, self._state_outputs)) if len(self.get_initial_state()) != len(self._state_inputs): raise ValueError( "Length of initial state must match number of state inputs, " "got: {} vs {}".format(self.get_initial_state(), self._state_inputs)) if self._state_inputs and self._seq_lens is None: raise ValueError( "seq_lens tensor must be given if state inputs are defined") # Generate the log-likelihood calculator. self._log_likelihood = log_likelihood
[docs] def variables(self): """Return the list of all savable variables for this policy.""" return self.model.variables()
[docs] def get_placeholder(self, name): """Returns the given action or loss input placeholder by name. If the loss has not been initialized and a loss input placeholder is requested, an error is raised. """ obs_inputs = { SampleBatch.CUR_OBS: self._obs_input, SampleBatch.PREV_ACTIONS: self._prev_action_input, SampleBatch.PREV_REWARDS: self._prev_reward_input, } if name in obs_inputs: return obs_inputs[name] assert self._loss_input_dict is not None, \ "Should have set this before get_placeholder can be called" return self._loss_input_dict[name]
[docs] def get_session(self): """Returns a reference to the TF session for this policy.""" return self._sess
[docs] def loss_initialized(self): """Returns whether the loss function has been initialized.""" return self._loss is not None
def _initialize_loss(self, loss, loss_inputs): self._loss_inputs = loss_inputs self._loss_input_dict = dict(self._loss_inputs) for i, ph in enumerate(self._state_inputs): self._loss_input_dict["state_in_{}".format(i)] = ph if self.model: self._loss = self.model.custom_loss(loss, self._loss_input_dict) self._stats_fetches.update({ "model": self.model.metrics() if isinstance( self.model, ModelV2) else self.model.custom_stats() }) else: self._loss = loss self._optimizer = self.optimizer() self._grads_and_vars = [ (g, v) for (g, v) in self.gradients(self._optimizer, self._loss) if g is not None ] self._grads = [g for (g, v) in self._grads_and_vars] # TODO(sven/ekl): Deprecate support for v1 models. if hasattr(self, "model") and isinstance(self.model, ModelV2): self._variables = ray.experimental.tf_utils.TensorFlowVariables( [], self._sess, self.variables()) else: self._variables = ray.experimental.tf_utils.TensorFlowVariables( self._loss, self._sess) # gather update ops for any batch norm layers if not self._update_ops: self._update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope=tf.get_variable_scope().name) if self._update_ops:"Update ops to run on apply gradient: {}".format( self._update_ops)) with tf.control_dependencies(self._update_ops): self._apply_op = self.build_apply_op(self._optimizer, self._grads_and_vars) if log_once("loss_used"): logger.debug( "These tensors were used in the loss_fn:\n\n{}\n".format( summarize(self._loss_input_dict)))
[docs] @override(Policy) def compute_actions(self, obs_batch, state_batches=None, prev_action_batch=None, prev_reward_batch=None, info_batch=None, episodes=None, explore=None, timestep=None, **kwargs): explore = explore if explore is not None else self.config["explore"] builder = TFRunBuilder(self._sess, "compute_actions") fetches = self._build_compute_actions( builder, obs_batch, state_batches, prev_action_batch, prev_reward_batch, explore=explore, timestep=timestep if timestep is not None else self.global_timestep) # Execute session run to get action (and other fetches). return builder.get(fetches)
[docs] @override(Policy) def compute_log_likelihoods(self, actions, obs_batch, state_batches=None, prev_action_batch=None, prev_reward_batch=None): if self._log_likelihood is None: raise ValueError("Cannot compute log-prob/likelihood w/o a " "self._log_likelihood op!") # Do the forward pass through the model to capture the parameters # for the action distribution, then do a logp on that distribution. builder = TFRunBuilder(self._sess, "compute_log_likelihoods") # Feed actions (for which we want logp values) into graph. builder.add_feed_dict({self._action_input: actions}) # Feed observations. builder.add_feed_dict({self._obs_input: obs_batch}) # Internal states. state_batches = state_batches or [] if len(self._state_inputs) != len(state_batches): raise ValueError( "Must pass in RNN state batches for placeholders {}, got {}". format(self._state_inputs, state_batches)) builder.add_feed_dict( {k: v for k, v in zip(self._state_inputs, state_batches)}) if state_batches: builder.add_feed_dict({self._seq_lens: np.ones(len(obs_batch))}) # Prev-a and r. if self._prev_action_input is not None and \ prev_action_batch is not None: builder.add_feed_dict({self._prev_action_input: prev_action_batch}) if self._prev_reward_input is not None and \ prev_reward_batch is not None: builder.add_feed_dict({self._prev_reward_input: prev_reward_batch}) # Fetch the log_likelihoods output and return. fetches = builder.add_fetches([self._log_likelihood]) return builder.get(fetches)[0]
[docs] @override(Policy) def compute_gradients(self, postprocessed_batch): assert self.loss_initialized() builder = TFRunBuilder(self._sess, "compute_gradients") fetches = self._build_compute_gradients(builder, postprocessed_batch) return builder.get(fetches)
[docs] @override(Policy) def apply_gradients(self, gradients): assert self.loss_initialized() builder = TFRunBuilder(self._sess, "apply_gradients") fetches = self._build_apply_gradients(builder, gradients) builder.get(fetches)
[docs] @override(Policy) def learn_on_batch(self, postprocessed_batch): assert self.loss_initialized() builder = TFRunBuilder(self._sess, "learn_on_batch") fetches = self._build_learn_on_batch(builder, postprocessed_batch) return builder.get(fetches)
[docs] @override(Policy) def get_exploration_info(self): if isinstance(self.exploration, Exploration): return
[docs] @override(Policy) def get_weights(self): return self._variables.get_weights()
[docs] @override(Policy) def set_weights(self, weights): return self._variables.set_weights(weights)
[docs] @override(Policy) def export_model(self, export_dir): """Export tensorflow graph to export_dir for serving.""" with self._sess.graph.as_default(): builder = tf.saved_model.builder.SavedModelBuilder(export_dir) signature_def_map = self._build_signature_def() builder.add_meta_graph_and_variables( self._sess, [tf.saved_model.tag_constants.SERVING], signature_def_map=signature_def_map)
[docs] @override(Policy) def export_checkpoint(self, export_dir, filename_prefix="model"): """Export tensorflow checkpoint to export_dir.""" try: os.makedirs(export_dir) except OSError as e: # ignore error if export dir already exists if e.errno != errno.EEXIST: raise save_path = os.path.join(export_dir, filename_prefix) with self._sess.graph.as_default(): saver = tf.train.Saver(), save_path)
[docs] @DeveloperAPI def copy(self, existing_inputs): """Creates a copy of self using existing input placeholders. Optional, only required to work with the multi-GPU optimizer.""" raise NotImplementedError
[docs] @override(Policy) def is_recurrent(self): return len(self._state_inputs) > 0
[docs] @override(Policy) def num_state_tensors(self): return len(self._state_inputs)
[docs] @DeveloperAPI def extra_compute_action_feed_dict(self): """Extra dict to pass to the compute actions session run.""" return {}
[docs] @DeveloperAPI def extra_compute_action_fetches(self): """Extra values to fetch and return from compute_actions(). By default we only return action probability info (if present). """ ret = {} if self._sampled_action_logp is not None: ret[ACTION_PROB] = self._sampled_action_prob ret[ACTION_LOGP] = self._sampled_action_logp return ret
[docs] @DeveloperAPI def extra_compute_grad_feed_dict(self): """Extra dict to pass to the compute gradients session run.""" return {} # e.g, kl_coeff
[docs] @DeveloperAPI def extra_compute_grad_fetches(self): """Extra values to fetch and return from compute_gradients().""" return {LEARNER_STATS_KEY: {}} # e.g, stats, td error, etc.
[docs] @DeveloperAPI def optimizer(self): """TF optimizer to use for policy optimization.""" if hasattr(self, "config"): return tf.train.AdamOptimizer(self.config["lr"]) else: return tf.train.AdamOptimizer()
[docs] @DeveloperAPI def gradients(self, optimizer, loss): """Override for custom gradient computation.""" return optimizer.compute_gradients(loss)
[docs] @DeveloperAPI def build_apply_op(self, optimizer, grads_and_vars): """Override for custom gradient apply computation.""" # specify global_step for TD3 which needs to count the num updates return optimizer.apply_gradients( self._grads_and_vars, global_step=tf.train.get_or_create_global_step())
@DeveloperAPI def _get_is_training_placeholder(self): """Get the placeholder for _is_training, i.e., for batch norm layers. This can be called safely before __init__ has run. """ if not hasattr(self, "_is_training"): self._is_training = tf.placeholder_with_default( False, (), name="is_training") return self._is_training def _debug_vars(self): if log_once("grad_vars"): for _, v in self._grads_and_vars:"Optimizing variable {}".format(v)) def _extra_input_signature_def(self): """Extra input signatures to add when exporting tf model. Inferred from extra_compute_action_feed_dict() """ feed_dict = self.extra_compute_action_feed_dict() return { tf.saved_model.utils.build_tensor_info(k) for k in feed_dict.keys() } def _extra_output_signature_def(self): """Extra output signatures to add when exporting tf model. Inferred from extra_compute_action_fetches() """ fetches = self.extra_compute_action_fetches() return { k: tf.saved_model.utils.build_tensor_info(fetches[k]) for k in fetches.keys() } def _build_signature_def(self): """Build signature def map for tensorflow SavedModelBuilder. """ # build input signatures input_signature = self._extra_input_signature_def() input_signature["observations"] = \ tf.saved_model.utils.build_tensor_info(self._obs_input) if self._seq_lens is not None: input_signature["seq_lens"] = \ tf.saved_model.utils.build_tensor_info(self._seq_lens) if self._prev_action_input is not None: input_signature["prev_action"] = \ tf.saved_model.utils.build_tensor_info(self._prev_action_input) if self._prev_reward_input is not None: input_signature["prev_reward"] = \ tf.saved_model.utils.build_tensor_info(self._prev_reward_input) input_signature["is_training"] = \ tf.saved_model.utils.build_tensor_info(self._is_training) for state_input in self._state_inputs: input_signature[] = \ tf.saved_model.utils.build_tensor_info(state_input) # build output signatures output_signature = self._extra_output_signature_def() output_signature["actions"] = \ tf.saved_model.utils.build_tensor_info(self._sampled_action) for state_output in self._state_outputs: output_signature[] = \ tf.saved_model.utils.build_tensor_info(state_output) signature_def = ( tf.saved_model.signature_def_utils.build_signature_def( input_signature, output_signature, tf.saved_model.signature_constants.PREDICT_METHOD_NAME)) signature_def_key = (tf.saved_model.signature_constants. DEFAULT_SERVING_SIGNATURE_DEF_KEY) signature_def_map = {signature_def_key: signature_def} return signature_def_map def _build_compute_actions(self, builder, obs_batch, state_batches=None, prev_action_batch=None, prev_reward_batch=None, episodes=None, explore=None, timestep=None): explore = explore if explore is not None else self.config["explore"] state_batches = state_batches or [] if len(self._state_inputs) != len(state_batches): raise ValueError( "Must pass in RNN state batches for placeholders {}, got {}". format(self._state_inputs, state_batches)) builder.add_feed_dict(self.extra_compute_action_feed_dict()) builder.add_feed_dict({self._obs_input: obs_batch}) if state_batches: builder.add_feed_dict({self._seq_lens: np.ones(len(obs_batch))}) if self._prev_action_input is not None and \ prev_action_batch is not None: builder.add_feed_dict({self._prev_action_input: prev_action_batch}) if self._prev_reward_input is not None and \ prev_reward_batch is not None: builder.add_feed_dict({self._prev_reward_input: prev_reward_batch}) builder.add_feed_dict({self._is_training: False}) builder.add_feed_dict({self._is_exploring: explore}) if timestep is not None: builder.add_feed_dict({self._timestep: timestep}) builder.add_feed_dict(dict(zip(self._state_inputs, state_batches))) fetches = builder.add_fetches([self._sampled_action] + self._state_outputs + [self.extra_compute_action_fetches()]) return fetches[0], fetches[1:-1], fetches[-1] def _build_compute_gradients(self, builder, postprocessed_batch): self._debug_vars() builder.add_feed_dict(self.extra_compute_grad_feed_dict()) builder.add_feed_dict({self._is_training: True}) builder.add_feed_dict( self._get_loss_inputs_dict(postprocessed_batch, shuffle=False)) fetches = builder.add_fetches( [self._grads, self._get_grad_and_stats_fetches()]) return fetches[0], fetches[1] def _build_apply_gradients(self, builder, gradients): if len(gradients) != len(self._grads): raise ValueError( "Unexpected number of gradients to apply, got {} for {}". format(gradients, self._grads)) builder.add_feed_dict({self._is_training: True}) builder.add_feed_dict(dict(zip(self._grads, gradients))) fetches = builder.add_fetches([self._apply_op]) return fetches[0] def _build_learn_on_batch(self, builder, postprocessed_batch): self._debug_vars() builder.add_feed_dict(self.extra_compute_grad_feed_dict()) builder.add_feed_dict( self._get_loss_inputs_dict(postprocessed_batch, shuffle=False)) builder.add_feed_dict({self._is_training: True}) fetches = builder.add_fetches([ self._apply_op, self._get_grad_and_stats_fetches(), ]) return fetches[1] def _get_grad_and_stats_fetches(self): fetches = self.extra_compute_grad_fetches() if LEARNER_STATS_KEY not in fetches: raise ValueError( "Grad fetches should contain 'stats': {...} entry") if self._stats_fetches: fetches[LEARNER_STATS_KEY] = dict(self._stats_fetches, **fetches[LEARNER_STATS_KEY]) return fetches def _get_loss_inputs_dict(self, batch, shuffle): """Return a feed dict from a batch. Arguments: batch (SampleBatch): batch of data to derive inputs from shuffle (bool): whether to shuffle batch sequences. Shuffle may be done in-place. This only makes sense if you're further applying minibatch SGD after getting the outputs. Returns: feed dict of data """ feed_dict = {} if self._batch_divisibility_req > 1: meets_divisibility_reqs = ( len(batch[SampleBatch.CUR_OBS]) % self._batch_divisibility_req == 0 and max(batch[SampleBatch.AGENT_INDEX]) == 0) # not multiagent else: meets_divisibility_reqs = True # Simple case: not RNN nor do we need to pad if not self._state_inputs and meets_divisibility_reqs: if shuffle: batch.shuffle() for k, ph in self._loss_inputs: feed_dict[ph] = batch[k] return feed_dict if self._state_inputs: max_seq_len = self._max_seq_len dynamic_max = True else: max_seq_len = self._batch_divisibility_req dynamic_max = False # RNN or multi-agent case feature_keys = [k for k, v in self._loss_inputs] state_keys = [ "state_in_{}".format(i) for i in range(len(self._state_inputs)) ] feature_sequences, initial_states, seq_lens = chop_into_sequences( batch[SampleBatch.EPS_ID], batch[SampleBatch.UNROLL_ID], batch[SampleBatch.AGENT_INDEX], [batch[k] for k in feature_keys], [batch[k] for k in state_keys], max_seq_len, dynamic_max=dynamic_max, shuffle=shuffle) for k, v in zip(feature_keys, feature_sequences): feed_dict[self._loss_input_dict[k]] = v for k, v in zip(state_keys, initial_states): feed_dict[self._loss_input_dict[k]] = v feed_dict[self._seq_lens] = seq_lens if log_once("rnn_feed_dict"):"Padded input for RNN:\n\n{}\n".format( summarize({ "features": feature_sequences, "initial_states": initial_states, "seq_lens": seq_lens, "max_seq_len": max_seq_len, }))) return feed_dict
@DeveloperAPI class LearningRateSchedule: """Mixin for TFPolicy that adds a learning rate schedule.""" @DeveloperAPI def __init__(self, lr, lr_schedule): self.cur_lr = tf.get_variable("lr", initializer=lr, trainable=False) if lr_schedule is None: self.lr_schedule = ConstantSchedule(lr) else: self.lr_schedule = PiecewiseSchedule( lr_schedule, outside_value=lr_schedule[-1][-1]) @override(Policy) def on_global_var_update(self, global_vars): super(LearningRateSchedule, self).on_global_var_update(global_vars) self.cur_lr.load( self.lr_schedule.value(global_vars["timestep"]), session=self._sess) @override(TFPolicy) def optimizer(self): return tf.train.AdamOptimizer(self.cur_lr) @DeveloperAPI class EntropyCoeffSchedule: """Mixin for TFPolicy that adds entropy coeff decay.""" @DeveloperAPI def __init__(self, entropy_coeff, entropy_coeff_schedule): self.entropy_coeff = tf.get_variable( "entropy_coeff", initializer=entropy_coeff, trainable=False) if entropy_coeff_schedule is None: self.entropy_coeff_schedule = ConstantSchedule(entropy_coeff) else: # Allows for custom schedule similar to lr_schedule format if isinstance(entropy_coeff_schedule, list): self.entropy_coeff_schedule = PiecewiseSchedule( entropy_coeff_schedule, outside_value=entropy_coeff_schedule[-1][-1]) else: # Implements previous version but enforces outside_value self.entropy_coeff_schedule = PiecewiseSchedule( [[0, entropy_coeff], [entropy_coeff_schedule, 0.0]], outside_value=0.0) @override(Policy) def on_global_var_update(self, global_vars): super(EntropyCoeffSchedule, self).on_global_var_update(global_vars) self.entropy_coeff.load( self.entropy_coeff_schedule.value(global_vars["timestep"]), session=self._sess)