hunk.code_window:                     "agent_id": agent_id,
                       "pre_batch": pre_batches[agent_id],
                       "post_batch": post_batch,
                       "all_pre_batches": pre_batches,
                   })
           self.agent_builders.clear()
           self.agent_to_policy.clear()
  
hunk.labels: keep keep keep keep add keep keep keep
hunk.after_edit:             self.policy_builders[self.agent_to_policy[agent_id]].add_batch(
                   post_batch)
  
hunk.file_path: rllib/evaluation/sample_batch_builder.py
hunk.type: add
file: import collections
  import logging
  import numpy as np
  
  from ray.util.debug import log_once
  from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch
  from ray.rllib.utils.annotations import PublicAPI, DeveloperAPI
  from ray.rllib.utils.debug import summarize
  
  logger = logging.getLogger(__name__)
  
  
  def to_float_array(v):
      arr = np.array(v)
      if arr.dtype == np.float64:
          return arr.astype(np.float32)  # save some memory
      return arr
  
  
  @PublicAPI
  class SampleBatchBuilder:
      """Util to build a SampleBatch incrementally.
  
      For efficiency, SampleBatches hold values in column form (as arrays).
      However, it is useful to add data one row (dict) at a time.
      """
  
      @PublicAPI
      def __init__(self):
          self.buffers = collections.defaultdict(list)
          self.count = 0
          self.unroll_id = 0  # disambiguates unrolls within a single episode
  
      @PublicAPI
      def add_values(self, **values):
          """Add the given dictionary (row) of values to this batch."""
  
          for k, v in values.items():
              self.buffers[k].append(v)
          self.count += 1
  
      @PublicAPI
      def add_batch(self, batch):
          """Add the given batch of values to this batch."""
  
          for k, column in batch.items():
              self.buffers[k].extend(column)
          self.count += batch.count
  
      @PublicAPI
      def build_and_reset(self):
          """Returns a sample batch including all previously added values."""
  
          batch = SampleBatch(
              {k: to_float_array(v)
               for k, v in self.buffers.items()})
          batch.data[SampleBatch.UNROLL_ID] = np.repeat(self.unroll_id,
                                                        batch.count)
          self.buffers.clear()
          self.count = 0
          self.unroll_id += 1
          return batch
  
  
  @DeveloperAPI
  class MultiAgentSampleBatchBuilder:
      """Util to build SampleBatches for each policy in a multi-agent env.
  
      Input data is per-agent, while output data is per-policy. There is an M:N
      mapping between agents and policies. We retain one local batch builder
      per agent. When an agent is done, then its local batch is appended into the
      corresponding policy batch for the agent's policy.
      """
  
      def __init__(self, policy_map, clip_rewards, postp_callback):
          """Initialize a MultiAgentSampleBatchBuilder.
  
          Arguments:
              policy_map (dict): Maps policy ids to policy instances.
              clip_rewards (bool): Whether to clip rewards before postprocessing.
              postp_callback: function to call on each postprocessed batch.
          """
  
          self.policy_map = policy_map
          self.clip_rewards = clip_rewards
          self.policy_builders = {
              k: SampleBatchBuilder()
              for k in policy_map.keys()
          }
          self.agent_builders = {}
          self.agent_to_policy = {}
          self.postp_callback = postp_callback
          self.count = 0  # increment this manually
  
      def total(self):
          """Returns summed number of steps across all agent buffers."""
  
          return sum(a.count for a in self.agent_builders.values())
  
      def has_pending_agent_data(self):
          """Returns whether there is pending unprocessed data."""
  
          return len(self.agent_builders) > 0
  
      @DeveloperAPI
      def add_values(self, agent_id, policy_id, **values):
          """Add the given dictionary (row) of values to this batch.
  
          Arguments:
              agent_id (obj): Unique id for the agent we are adding values for.
              policy_id (obj): Unique id for policy controlling the agent.
              values (dict): Row of values to add for this agent.
          """
  
          if agent_id not in self.agent_builders:
              self.agent_builders[agent_id] = SampleBatchBuilder()
              self.agent_to_policy[agent_id] = policy_id
          builder = self.agent_builders[agent_id]
          builder.add_values(**values)
  
      def postprocess_batch_so_far(self, episode):
          """Apply policy postprocessors to any unprocessed rows.
  
          This pushes the postprocessed per-agent batches onto the per-policy
          builders, clearing per-agent state.
  
          Arguments:
              episode: current MultiAgentEpisode object or None
          """
  
          # Materialize the batches so far
          pre_batches = {}
          for agent_id, builder in self.agent_builders.items():
              pre_batches[agent_id] = (
                  self.policy_map[self.agent_to_policy[agent_id]],
                  builder.build_and_reset())
  
          # Apply postprocessor
          post_batches = {}
          if self.clip_rewards:
              for _, (_, pre_batch) in pre_batches.items():
                  pre_batch["rewards"] = np.sign(pre_batch["rewards"])
          for agent_id, (_, pre_batch) in pre_batches.items():
              other_batches = pre_batches.copy()
              del other_batches[agent_id]
              policy = self.policy_map[self.agent_to_policy[agent_id]]
              if any(pre_batch["dones"][:-1]) or len(set(
                      pre_batch["eps_id"])) > 1:
                  raise ValueError(
                      "Batches sent to postprocessing must only contain steps "
                      "from a single trajectory.", pre_batch)
              post_batches[agent_id] = policy.postprocess_trajectory(
                  pre_batch, other_batches, episode)
              # Call the Policy's Exploration's postprocess method.
              policy.exploration.postprocess_trajectory(
                  policy, post_batches[agent_id], getattr(policy, "_sess", None))
  
          if log_once("after_post"):
              logger.info(
                  "Trajectory fragment after postprocess_trajectory():\n\n{}\n".
                  format(summarize(post_batches)))
  
          # Append into policy batches and reset
          for agent_id, post_batch in sorted(post_batches.items()):
              self.policy_builders[self.agent_to_policy[agent_id]].add_batch(
                  post_batch)
              if self.postp_callback:
                  self.postp_callback({
                      "episode": episode,
                      "agent_id": agent_id,
                      "pre_batch": pre_batches[agent_id],
                      "post_batch": post_batch,
                      "all_pre_batches": pre_batches,
                  })
  
          self.agent_builders.clear()
          self.agent_to_policy.clear()
  
      def check_missing_dones(self):
          for agent_id, builder in self.agent_builders.items():
              if builder.buffers["dones"][-1] is not True:
                  raise ValueError(
                      "The environment terminated for all agents, but we still "
                      "don't have a last observation for "
                      "agent {} (policy {}). ".format(
                          agent_id, self.agent_to_policy[agent_id]) +
                      "Please ensure that you include the last observations "
                      "of all live agents when setting '__all__' done to True. "
                      "Alternatively, set no_done_at_end=True to allow this.")
  
      @DeveloperAPI
      def build_and_reset(self, episode):
          """Returns the accumulated sample batches for each policy.
  
          Any unprocessed rows will be first postprocessed with a policy
          postprocessor. The internal state of this builder will be reset.
  
          Arguments:
              episode: current MultiAgentEpisode object or None
          """
  
          self.postprocess_batch_so_far(episode)
          policy_batches = {}
          for policy_id, builder in self.policy_builders.items():
              if builder.count > 0:
                  policy_batches[policy_id] = builder.build_and_reset()
          old_count = self.count
          self.count = 0
          return MultiAgentBatch.wrap_as_needed(policy_batches, old_count)
  