Source code for ray.rllib.evaluation.sample_batch_builder

import collections
import logging
import numpy as np

from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch
from ray.rllib.utils.annotations import PublicAPI, DeveloperAPI
from ray.rllib.utils.debug import log_once, 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


[docs]@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
[docs] @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
[docs] @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
[docs] @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
[docs]@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
[docs] def total(self): """Returns summed number of steps across all agent buffers.""" return sum(p.count for p in self.policy_builders.values())
[docs] def has_pending_data(self): """Returns whether there is pending unprocessed data.""" return len(self.agent_builders) > 0
[docs] @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)
[docs] 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) 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.")
[docs] @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)