hunk.code_window:     # Unsquash actions to the upper and lower bounds of env's action space.
       "normalize_actions": True,
       # === Learning ===
       # Update the target by \tau * policy + (1-\tau) * target_policy
       "tau": 5e-3,
       # Target entropy lower bound. This is the inverse of reward scale,
       # and will be optimized automatically.
       "target_entropy": "auto",
  
hunk.labels: keep keep keep keep replace keep keep keep keep
hunk.after_edit:     # Update the target by \tau * policy + (1-\tau) * target_policy.
  
hunk.file_path: rllib/agents/sac/sac.py
hunk.type: replace
file: """Eager mode TF policy built using build_tf_policy().
  
  It supports both traced and non-traced eager execution modes."""
  
  import logging
  import functools
  import numpy as np
  
  from ray.util.debug import log_once
  from ray.rllib.evaluation.episode import _flatten_action
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.policy import ACTION_PROB, ACTION_LOGP
  from ray.rllib.utils import add_mixins
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
  
  tf = try_import_tf()
  logger = logging.getLogger(__name__)
  
  
  def _convert_to_tf(x):
      if isinstance(x, SampleBatch):
          x = {k: v for k, v in x.items() if k != SampleBatch.INFOS}
          return tf.nest.map_structure(_convert_to_tf, x)
      if isinstance(x, Policy):
          return x
  
      if x is not None:
          x = tf.nest.map_structure(
              lambda f: tf.convert_to_tensor(f) if f is not None else None, x)
      return x
  
  
  def _convert_to_numpy(x):
      if x is None:
          return None
      try:
          return tf.nest.map_structure(lambda component: component.numpy(), x)
      except AttributeError:
          raise TypeError(
              ("Object of type {} has no method to convert to numpy.").format(
                  type(x)))
  
  
  def convert_eager_inputs(func):
      @functools.wraps(func)
      def _func(*args, **kwargs):
          if tf.executing_eagerly():
              args = [_convert_to_tf(x) for x in args]
              # TODO(gehring): find a way to remove specific hacks
              kwargs = {
                  k: _convert_to_tf(v)
                  for k, v in kwargs.items()
                  if k not in {"info_batch", "episodes"}
              }
          return func(*args, **kwargs)
  
      return _func
  
  
  def convert_eager_outputs(func):
      @functools.wraps(func)
      def _func(*args, **kwargs):
          out = func(*args, **kwargs)
          if tf.executing_eagerly():
              out = tf.nest.map_structure(_convert_to_numpy, out)
          return out
  
      return _func
  
  
  def _disallow_var_creation(next_creator, **kw):
      v = next_creator(**kw)
      raise ValueError("Detected a variable being created during an eager "
                       "forward pass. Variables should only be created during "
                       "model initialization: {}".format(v.name))
  
  
  def traced_eager_policy(eager_policy_cls):
      """Wrapper that enables tracing for all eager policy methods.
  
      This is enabled by the --trace / "eager_tracing" config."""
  
      class TracedEagerPolicy(eager_policy_cls):
          def __init__(self, *args, **kwargs):
              self._traced_learn_on_batch = None
              self._traced_compute_actions = None
              self._traced_compute_gradients = None
              self._traced_apply_gradients = None
              super(TracedEagerPolicy, self).__init__(*args, **kwargs)
  
          @override(Policy)
          @convert_eager_inputs
          @convert_eager_outputs
          def learn_on_batch(self, samples):
  
              if self._traced_learn_on_batch is None:
                  self._traced_learn_on_batch = tf.function(
                      super(TracedEagerPolicy, self).learn_on_batch,
                      autograph=False)
  
              return self._traced_learn_on_batch(samples)
  
          @override(Policy)
          @convert_eager_inputs
          @convert_eager_outputs
          def compute_actions(self,
                              obs_batch,
                              state_batches,
                              prev_action_batch=None,
                              prev_reward_batch=None,
                              info_batch=None,
                              episodes=None,
                              explore=None,
                              timestep=None,
                              **kwargs):
  
              obs_batch = tf.convert_to_tensor(obs_batch)
              state_batches = _convert_to_tf(state_batches)
              prev_action_batch = _convert_to_tf(prev_action_batch)
              prev_reward_batch = _convert_to_tf(prev_reward_batch)
  
              if self._traced_compute_actions is None:
                  self._traced_compute_actions = tf.function(
                      super(TracedEagerPolicy, self).compute_actions,
                      autograph=False)
  
              return self._traced_compute_actions(
                  obs_batch, state_batches, prev_action_batch, prev_reward_batch,
                  info_batch, episodes, explore, timestep, **kwargs)
  
          @override(Policy)
          @convert_eager_inputs
          @convert_eager_outputs
          def compute_gradients(self, samples):
  
              if self._traced_compute_gradients is None:
                  self._traced_compute_gradients = tf.function(
                      super(TracedEagerPolicy, self).compute_gradients,
                      autograph=False)
  
              return self._traced_compute_gradients(samples)
  
          @override(Policy)
          @convert_eager_inputs
          @convert_eager_outputs
          def apply_gradients(self, grads):
  
              if self._traced_apply_gradients is None:
                  self._traced_apply_gradients = tf.function(
                      super(TracedEagerPolicy, self).apply_gradients,
                      autograph=False)
  
              return self._traced_apply_gradients(grads)
  
      TracedEagerPolicy.__name__ = eager_policy_cls.__name__
      TracedEagerPolicy.__qualname__ = eager_policy_cls.__qualname__
      return TracedEagerPolicy
  
  
  def build_eager_tf_policy(name,
                            loss_fn,
                            get_default_config=None,
                            postprocess_fn=None,
                            stats_fn=None,
                            optimizer_fn=None,
                            gradients_fn=None,
                            apply_gradients_fn=None,
                            grad_stats_fn=None,
                            extra_learn_fetches_fn=None,
                            extra_action_fetches_fn=None,
                            before_init=None,
                            before_loss_init=None,
                            after_init=None,
                            make_model=None,
                            action_sampler_fn=None,
                            log_likelihood_fn=None,
                            mixins=None,
                            obs_include_prev_action_reward=True,
                            get_batch_divisibility_req=None):
      """Build an eager TF policy.
  
      An eager policy runs all operations in eager mode, which makes debugging
      much simpler, but has lower performance.
  
      You shouldn't need to call this directly. Rather, prefer to build a TF
      graph policy and use set {"eager": true} in the trainer config to have
      it automatically be converted to an eager policy.
  
      This has the same signature as build_tf_policy()."""
  
      base = add_mixins(Policy, mixins)
  
      class eager_policy_cls(base):
          def __init__(self, observation_space, action_space, config):
              assert tf.executing_eagerly()
              self.framework = "tf"
              Policy.__init__(self, observation_space, action_space, config)
              self._is_training = False
              self._loss_initialized = False
              self._sess = None
  
              if get_default_config:
                  config = dict(get_default_config(), **config)
  
              if before_init:
                  before_init(self, observation_space, action_space, config)
  
              self.config = config
              self.dist_class = None
  
              if action_sampler_fn:
                  if not make_model:
                      raise ValueError("`make_model` is required if "
                                       "`action_sampler_fn` is given")
              else:
                  self.dist_class, logit_dim = ModelCatalog.get_action_dist(
                      action_space, self.config["model"])
  
              if make_model:
                  self.model = make_model(self, observation_space, action_space,
                                          config)
              else:
                  self.model = ModelCatalog.get_model_v2(
                      observation_space,
                      action_space,
                      logit_dim,
                      config["model"],
                      framework="tf",
                  )
  
              self._state_in = [
                  tf.convert_to_tensor(np.array([s]))
                  for s in self.model.get_initial_state()
              ]
  
              input_dict = {
                  SampleBatch.CUR_OBS: tf.convert_to_tensor(
                      np.array([observation_space.sample()])),
                  SampleBatch.PREV_ACTIONS: tf.convert_to_tensor(
                      [_flatten_action(action_space.sample())]),
                  SampleBatch.PREV_REWARDS: tf.convert_to_tensor([0.]),
              }
              self.model(input_dict, self._state_in, tf.convert_to_tensor([1]))
  
              if before_loss_init:
                  before_loss_init(self, observation_space, action_space, config)
  
              self._initialize_loss_with_dummy_batch()
              self._loss_initialized = True
  
              if optimizer_fn:
                  self._optimizer = optimizer_fn(self, config)
              else:
                  self._optimizer = tf.train.AdamOptimizer(config["lr"])
  
              if after_init:
                  after_init(self, observation_space, action_space, config)
  
          @override(Policy)
          def postprocess_trajectory(self,
                                     samples,
                                     other_agent_batches=None,
                                     episode=None):
              assert tf.executing_eagerly()
              if postprocess_fn:
                  return postprocess_fn(self, samples, other_agent_batches,
                                        episode)
              else:
                  return samples
  
          @override(Policy)
          @convert_eager_inputs
          @convert_eager_outputs
          def learn_on_batch(self, samples):
              with tf.variable_creator_scope(_disallow_var_creation):
                  grads_and_vars, stats = self._compute_gradients(samples)
              self._apply_gradients(grads_and_vars)
              return stats
  
          @override(Policy)
          @convert_eager_inputs
          @convert_eager_outputs
          def compute_gradients(self, samples):
              with tf.variable_creator_scope(_disallow_var_creation):
                  grads_and_vars, stats = self._compute_gradients(samples)
              grads = [g for g, v in grads_and_vars]
              return grads, stats
  
          @override(Policy)
          @convert_eager_inputs
          @convert_eager_outputs
          def compute_actions(self,
                              obs_batch,
                              state_batches,
                              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"]
  
              # TODO: remove python side effect to cull sources of bugs.
              self._is_training = False
              self._state_in = state_batches
  
              if tf.executing_eagerly():
                  n = len(obs_batch)
              else:
                  n = obs_batch.shape[0]
              seq_lens = tf.ones(n, dtype=tf.int32)
  
              input_dict = {
                  SampleBatch.CUR_OBS: tf.convert_to_tensor(obs_batch),
                  "is_training": tf.constant(False),
              }
              if obs_include_prev_action_reward:
                  input_dict.update({
                      SampleBatch.PREV_ACTIONS: tf.convert_to_tensor(
                          prev_action_batch),
                      SampleBatch.PREV_REWARDS: tf.convert_to_tensor(
                          prev_reward_batch),
                  })
  
              # Custom sampler fn given (which may handle self.exploration).
              if action_sampler_fn is not None:
                  state_out = []
                  action, logp = action_sampler_fn(
                      self, self.model, input_dict, self.observation_space,
                      self.action_space, explore, self.config, timestep)
              # Use Exploration object.
              else:
                  with tf.variable_creator_scope(_disallow_var_creation):
                      model_out, state_out = self.model(input_dict,
                                                        state_batches, seq_lens)
                      action, logp = self.exploration.get_exploration_action(
                          model_out,
                          self.dist_class,
                          self.model,
                          explore=explore,
                          timestep=timestep
                          if timestep is not None else self.global_timestep)
  
              extra_fetches = {}
              if logp is not None:
                  extra_fetches.update({
                      ACTION_PROB: tf.exp(logp),
                      ACTION_LOGP: logp,
                  })
              if extra_action_fetches_fn:
                  extra_fetches.update(extra_action_fetches_fn(self))
  
              # Increase our global sampling timestep counter by 1.
              self.global_timestep += 1
  
              return action, state_out, extra_fetches
  
          @override(Policy)
          def compute_log_likelihoods(self,
                                      actions,
                                      obs_batch,
                                      state_batches=None,
                                      prev_action_batch=None,
                                      prev_reward_batch=None):
  
              seq_lens = tf.ones(len(obs_batch), dtype=tf.int32)
              input_dict = {
                  SampleBatch.CUR_OBS: tf.convert_to_tensor(obs_batch),
                  "is_training": tf.constant(False),
              }
              if obs_include_prev_action_reward:
                  input_dict.update({
                      SampleBatch.PREV_ACTIONS: tf.convert_to_tensor(
                          prev_action_batch),
                      SampleBatch.PREV_REWARDS: tf.convert_to_tensor(
                          prev_reward_batch),
                  })
  
              # Custom log_likelihood function given.
              if log_likelihood_fn:
                  log_likelihoods = log_likelihood_fn(
                      self, self.model, actions, input_dict,
                      self.observation_space, self.action_space, self.config)
              # Default log-likelihood calculation.
              else:
                  dist_inputs, _ = self.model(input_dict, state_batches,
                                              seq_lens)
                  action_dist = self.dist_class(dist_inputs, self.model)
                  log_likelihoods = action_dist.logp(actions)
  
              return log_likelihoods
  
          @override(Policy)
          def apply_gradients(self, gradients):
              self._apply_gradients(
                  zip([(tf.convert_to_tensor(g) if g is not None else None)
                       for g in gradients], self.model.trainable_variables()))
  
          @override(Policy)
          def get_exploration_info(self):
              return _convert_to_numpy(self.exploration.get_info())
  
          @override(Policy)
          def get_weights(self):
              variables = self.variables()
              return [v.numpy() for v in variables]
  
          @override(Policy)
          def set_weights(self, weights):
              variables = self.variables()
              assert len(weights) == len(variables), (len(weights),
                                                      len(variables))
              for v, w in zip(variables, weights):
                  v.assign(w)
  
          def variables(self):
              """Return the list of all savable variables for this policy."""
              return self.model.variables()
  
          @override(Policy)
          def is_recurrent(self):
              return len(self._state_in) > 0
  
          @override(Policy)
          def num_state_tensors(self):
              return len(self._state_in)
  
          @override(Policy)
          def get_initial_state(self):
              return self.model.get_initial_state()
  
          def get_session(self):
              return None  # None implies eager
  
          def get_placeholder(self, ph):
              raise ValueError(
                  "get_placeholder() is not allowed in eager mode. Try using "
                  "rllib.utils.tf_ops.make_tf_callable() to write "
                  "functions that work in both graph and eager mode.")
  
          def loss_initialized(self):
              return self._loss_initialized
  
          @override(Policy)
          def export_model(self, export_dir):
              pass
  
          @override(Policy)
          def export_checkpoint(self, export_dir):
              pass
  
          def _get_is_training_placeholder(self):
              return tf.convert_to_tensor(self._is_training)
  
          def _apply_gradients(self, grads_and_vars):
              if apply_gradients_fn:
                  apply_gradients_fn(self, self._optimizer, grads_and_vars)
              else:
                  self._optimizer.apply_gradients(grads_and_vars)
  
          def _compute_gradients(self, samples):
              """Computes and returns grads as eager tensors."""
  
              self._is_training = True
  
              with tf.GradientTape(persistent=gradients_fn is not None) as tape:
                  # TODO: set seq len and state-in properly
                  state_in = []
                  for i in range(self.num_state_tensors()):
                      state_in.append(samples["state_in_{}".format(i)])
                  self._state_in = state_in
  
                  self._seq_lens = None
                  if len(state_in) > 0:
                      self._seq_lens = tf.ones(
                          samples[SampleBatch.CUR_OBS].shape[0], dtype=tf.int32)
                      samples["seq_lens"] = self._seq_lens
  
                  model_out, _ = self.model(samples, self._state_in,
                                            self._seq_lens)
                  loss = loss_fn(self, self.model, self.dist_class, samples)
  
              variables = self.model.trainable_variables()
  
              if gradients_fn:
  
                  class OptimizerWrapper:
                      def __init__(self, tape):
                          self.tape = tape
  
                      def compute_gradients(self, loss, var_list):
                          return list(
                              zip(self.tape.gradient(loss, var_list), var_list))
  
                  grads_and_vars = gradients_fn(self, OptimizerWrapper(tape),
                                                loss)
              else:
                  grads_and_vars = list(
                      zip(tape.gradient(loss, variables), variables))
  
              if log_once("grad_vars"):
                  for _, v in grads_and_vars:
                      logger.info("Optimizing variable {}".format(v.name))
  
              grads = [g for g, v in grads_and_vars]
              stats = self._stats(self, samples, grads)
              return grads_and_vars, stats
  
          def _stats(self, outputs, samples, grads):
  
              fetches = {}
              if stats_fn:
                  fetches[LEARNER_STATS_KEY] = {
                      k: v
                      for k, v in stats_fn(outputs, samples).items()
                  }
              else:
                  fetches[LEARNER_STATS_KEY] = {}
              if extra_learn_fetches_fn:
                  fetches.update(
                      {k: v
                       for k, v in extra_learn_fetches_fn(self).items()})
              if grad_stats_fn:
                  fetches.update({
                      k: v
                      for k, v in grad_stats_fn(self, samples, grads).items()
                  })
              return fetches
  
          def _initialize_loss_with_dummy_batch(self):
              # Dummy forward pass to initialize any policy attributes, etc.
              action_dtype, action_shape = ModelCatalog.get_action_shape(
                  self.action_space)
              dummy_batch = {
                  SampleBatch.CUR_OBS: tf.convert_to_tensor(
                      np.array([self.observation_space.sample()])),
                  SampleBatch.NEXT_OBS: tf.convert_to_tensor(
                      np.array([self.observation_space.sample()])),
                  SampleBatch.DONES: tf.convert_to_tensor(
                      np.array([False], dtype=np.bool)),
                  SampleBatch.ACTIONS: tf.convert_to_tensor(
                      np.zeros(
                          (1, ) + action_shape[1:],
                          dtype=action_dtype.as_numpy_dtype())),
                  SampleBatch.REWARDS: tf.convert_to_tensor(
                      np.array([0], dtype=np.float32)),
              }
              if obs_include_prev_action_reward:
                  dummy_batch.update({
                      SampleBatch.PREV_ACTIONS: dummy_batch[SampleBatch.ACTIONS],
                      SampleBatch.PREV_REWARDS: dummy_batch[SampleBatch.REWARDS],
                  })
              for i, h in enumerate(self._state_in):
                  dummy_batch["state_in_{}".format(i)] = h
                  dummy_batch["state_out_{}".format(i)] = h
  
              if self._state_in:
                  dummy_batch["seq_lens"] = tf.convert_to_tensor(
                      np.array([1], dtype=np.int32))
  
              # for IMPALA which expects a certain sample batch size.
              def tile_to(tensor, n):
                  return tf.tile(tensor,
                                 [n] + [1 for _ in tensor.shape.as_list()[1:]])
  
              if get_batch_divisibility_req:
                  dummy_batch = {
                      k: tile_to(v, get_batch_divisibility_req(self))
                      for k, v in dummy_batch.items()
                  }
  
              # Execute a forward pass to get self.action_dist etc initialized,
              # and also obtain the extra action fetches
              _, _, fetches = self.compute_actions(
                  dummy_batch[SampleBatch.CUR_OBS], self._state_in,
                  dummy_batch.get(SampleBatch.PREV_ACTIONS),
                  dummy_batch.get(SampleBatch.PREV_REWARDS))
              dummy_batch.update(fetches)
  
              postprocessed_batch = self.postprocess_trajectory(
                  SampleBatch(dummy_batch))
  
              # model forward pass for the loss (needed after postprocess to
              # overwrite any tensor state from that call)
              self.model.from_batch(dummy_batch)
  
              postprocessed_batch = {
                  k: tf.convert_to_tensor(v)
                  for k, v in postprocessed_batch.items()
              }
  
              loss_fn(self, self.model, self.dist_class, postprocessed_batch)
              if stats_fn:
                  stats_fn(self, postprocessed_batch)
  
          @classmethod
          def with_tracing(cls):
              return traced_eager_policy(cls)
  
      eager_policy_cls.__name__ = name + "_eager"
      eager_policy_cls.__qualname__ = name + "_eager"
      return eager_policy_cls
  