hunk.code_window:                 tmp.append(batch)
               batches = tmp
               for batch in batches:
                   self.replay_buffer.append(batch)
                   self.num_steps_sampled += batch.count
                   self.buffer_size += batch.count
  
hunk.labels: keep keep keep add keep keep keep
hunk.after_edit:                 if batch.count > self.max_buffer_size:
                       raise ValueError(
                           "The size of a single sample batch exceeds the replay "
                           "buffer size ({} > {})".format(batch.count,
                                                          self.max_buffer_size))
  
hunk.file_path: python/ray/rllib/optimizers/sync_batch_replay_optimizer.py
hunk.type: add
file: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from collections import OrderedDict
  import cv2
  import logging
  import numpy as np
  import gym
  
  from ray.rllib.utils.annotations import override, PublicAPI
  
  ATARI_OBS_SHAPE = (210, 160, 3)
  ATARI_RAM_OBS_SHAPE = (128, )
  VALIDATION_INTERVAL = 100
  
  logger = logging.getLogger(__name__)
  
  
  @PublicAPI
  class Preprocessor(object):
      """Defines an abstract observation preprocessor function.
  
      Attributes:
          shape (obj): Shape of the preprocessed output.
      """
  
      @PublicAPI
      def __init__(self, obs_space, options=None):
          legacy_patch_shapes(obs_space)
          self._obs_space = obs_space
          self._options = options or {}
          self.shape = self._init_shape(obs_space, options)
          self._size = int(np.product(self.shape))
          self._i = 0
  
      @PublicAPI
      def _init_shape(self, obs_space, options):
          """Returns the shape after preprocessing."""
          raise NotImplementedError
  
      @PublicAPI
      def transform(self, observation):
          """Returns the preprocessed observation."""
          raise NotImplementedError
  
      def write(self, observation, array, offset):
          """Alternative to transform for more efficient flattening."""
          array[offset:offset + self._size] = self.transform(observation)
  
      def check_shape(self, observation):
          """Checks the shape of the given observation."""
          if self._i % VALIDATION_INTERVAL == 0:
              if type(observation) is list and isinstance(
                      self._obs_space, gym.spaces.Box):
                  observation = np.array(observation)
              try:
                  if not self._obs_space.contains(observation):
                      raise ValueError(
                          "Observation outside expected value range",
                          self._obs_space, observation)
              except AttributeError:
                  raise ValueError(
                      "Observation for a Box space should be an np.array, "
                      "not a Python list.", observation)
          self._i += 1
  
      @property
      @PublicAPI
      def size(self):
          return self._size
  
      @property
      @PublicAPI
      def observation_space(self):
          obs_space = gym.spaces.Box(-1.0, 1.0, self.shape, dtype=np.float32)
          # Stash the unwrapped space so that we can unwrap dict and tuple spaces
          # automatically in model.py
          if (isinstance(self, TupleFlatteningPreprocessor)
                  or isinstance(self, DictFlatteningPreprocessor)):
              obs_space.original_space = self._obs_space
          return obs_space
  
  
  class GenericPixelPreprocessor(Preprocessor):
      """Generic image preprocessor.
  
      Note: for Atari games, use config {"preprocessor_pref": "deepmind"}
      instead for deepmind-style Atari preprocessing.
      """
  
      @override(Preprocessor)
      def _init_shape(self, obs_space, options):
          self._grayscale = options.get("grayscale")
          self._zero_mean = options.get("zero_mean")
          self._dim = options.get("dim")
          if self._grayscale:
              shape = (self._dim, self._dim, 1)
          else:
              shape = (self._dim, self._dim, 3)
  
          return shape
  
      @override(Preprocessor)
      def transform(self, observation):
          """Downsamples images from (210, 160, 3) by the configured factor."""
          self.check_shape(observation)
          scaled = observation[25:-25, :, :]
          if self._dim < 84:
              scaled = cv2.resize(scaled, (84, 84))
          # OpenAI: Resize by half, then down to 42x42 (essentially mipmapping).
          # If we resize directly we lose pixels that, when mapped to 42x42,
          # aren't close enough to the pixel boundary.
          scaled = cv2.resize(scaled, (self._dim, self._dim))
          if self._grayscale:
              scaled = scaled.mean(2)
              scaled = scaled.astype(np.float32)
              # Rescale needed for maintaining 1 channel
              scaled = np.reshape(scaled, [self._dim, self._dim, 1])
          if self._zero_mean:
              scaled = (scaled - 128) / 128
          else:
              scaled *= 1.0 / 255.0
          return scaled
  
  
  class AtariRamPreprocessor(Preprocessor):
      @override(Preprocessor)
      def _init_shape(self, obs_space, options):
          return (128, )
  
      @override(Preprocessor)
      def transform(self, observation):
          self.check_shape(observation)
          return (observation - 128) / 128
  
  
  class OneHotPreprocessor(Preprocessor):
      @override(Preprocessor)
      def _init_shape(self, obs_space, options):
          return (self._obs_space.n, )
  
      @override(Preprocessor)
      def transform(self, observation):
          self.check_shape(observation)
          arr = np.zeros(self._obs_space.n)
          arr[observation] = 1
          return arr
  
      @override(Preprocessor)
      def write(self, observation, array, offset):
          array[offset + observation] = 1
  
  
  class NoPreprocessor(Preprocessor):
      @override(Preprocessor)
      def _init_shape(self, obs_space, options):
          return self._obs_space.shape
  
      @override(Preprocessor)
      def transform(self, observation):
          self.check_shape(observation)
          return observation
  
      @override(Preprocessor)
      def write(self, observation, array, offset):
          array[offset:offset + self._size] = np.array(
              observation, copy=False).ravel()
  
  
  class TupleFlatteningPreprocessor(Preprocessor):
      """Preprocesses each tuple element, then flattens it all into a vector.
  
      RLlib models will unpack the flattened output before _build_layers_v2().
      """
  
      @override(Preprocessor)
      def _init_shape(self, obs_space, options):
          assert isinstance(self._obs_space, gym.spaces.Tuple)
          size = 0
          self.preprocessors = []
          for i in range(len(self._obs_space.spaces)):
              space = self._obs_space.spaces[i]
              logger.debug("Creating sub-preprocessor for {}".format(space))
              preprocessor = get_preprocessor(space)(space, self._options)
              self.preprocessors.append(preprocessor)
              size += preprocessor.size
          return (size, )
  
      @override(Preprocessor)
      def transform(self, observation):
          self.check_shape(observation)
          array = np.zeros(self.shape)
          self.write(observation, array, 0)
          return array
  
      @override(Preprocessor)
      def write(self, observation, array, offset):
          assert len(observation) == len(self.preprocessors), observation
          for o, p in zip(observation, self.preprocessors):
              p.write(o, array, offset)
              offset += p.size
  
  
  class DictFlatteningPreprocessor(Preprocessor):
      """Preprocesses each dict value, then flattens it all into a vector.
  
      RLlib models will unpack the flattened output before _build_layers_v2().
      """
  
      @override(Preprocessor)
      def _init_shape(self, obs_space, options):
          assert isinstance(self._obs_space, gym.spaces.Dict)
          size = 0
          self.preprocessors = []
          for space in self._obs_space.spaces.values():
              logger.debug("Creating sub-preprocessor for {}".format(space))
              preprocessor = get_preprocessor(space)(space, self._options)
              self.preprocessors.append(preprocessor)
              size += preprocessor.size
          return (size, )
  
      @override(Preprocessor)
      def transform(self, observation):
          self.check_shape(observation)
          array = np.zeros(self.shape)
          self.write(observation, array, 0)
          return array
  
      @override(Preprocessor)
      def write(self, observation, array, offset):
          if not isinstance(observation, OrderedDict):
              observation = OrderedDict(sorted(list(observation.items())))
          assert len(observation) == len(self.preprocessors), \
              (len(observation), len(self.preprocessors))
          for o, p in zip(observation.values(), self.preprocessors):
              p.write(o, array, offset)
              offset += p.size
  
  
  @PublicAPI
  def get_preprocessor(space):
      """Returns an appropriate preprocessor class for the given space."""
  
      legacy_patch_shapes(space)
      obs_shape = space.shape
  
      if isinstance(space, gym.spaces.Discrete):
          preprocessor = OneHotPreprocessor
      elif obs_shape == ATARI_OBS_SHAPE:
          preprocessor = GenericPixelPreprocessor
      elif obs_shape == ATARI_RAM_OBS_SHAPE:
          preprocessor = AtariRamPreprocessor
      elif isinstance(space, gym.spaces.Tuple):
          preprocessor = TupleFlatteningPreprocessor
      elif isinstance(space, gym.spaces.Dict):
          preprocessor = DictFlatteningPreprocessor
      else:
          preprocessor = NoPreprocessor
  
      return preprocessor
  
  
  def legacy_patch_shapes(space):
      """Assigns shapes to spaces that don't have shapes.
  
      This is only needed for older gym versions that don't set shapes properly
      for Tuple and Discrete spaces.
      """
  
      if not hasattr(space, "shape"):
          if isinstance(space, gym.spaces.Discrete):
              space.shape = ()
          elif isinstance(space, gym.spaces.Tuple):
              shapes = []
              for s in space.spaces:
                  shape = legacy_patch_shapes(s)
                  shapes.append(shape)
              space.shape = tuple(shapes)
  
      return space.shape
  