Source code for ray.rllib.evaluation.sample_batch_builder

import collections
import logging
import numpy as np
from typing import List, Any, Dict, Optional, TYPE_CHECKING

from ray.rllib.env.base_env import _DUMMY_AGENT_ID
from ray.rllib.evaluation.episode import MultiAgentEpisode
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch
from ray.rllib.utils.annotations import PublicAPI, DeveloperAPI
from ray.rllib.utils.debug import summarize
from ray.rllib.utils.deprecation import deprecation_warning
from ray.rllib.utils.typing import PolicyID, AgentID
from ray.util.debug import log_once

    from ray.rllib.agents.callbacks import DefaultCallbacks

logger = logging.getLogger(__name__)

def to_float_array(v: List[Any]) -> np.ndarray:
    arr = np.array(v)
    if arr.dtype == np.float64:
        return arr.astype(np.float32)  # save some memory
    return arr

# Deprecated class: Use a child class of `SampleCollector` instead.
[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. """ _next_unroll_id = 0 # disambiguates unrolls within a single episode @PublicAPI def __init__(self): if log_once("SampleBatchBuilder"): deprecation_warning( old="SampleBatchBuilder", new="child class of `SampleCollector`", error=False) self.buffers: Dict[str, List] = collections.defaultdict(list) self.count = 0
[docs] @PublicAPI def add_values(self, **values: Any) -> None: """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: SampleBatch) -> None: """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) -> SampleBatch: """Returns a sample batch including all previously added values.""" batch = SampleBatch( {k: to_float_array(v) for k, v in self.buffers.items()}) if SampleBatch.UNROLL_ID not in batch: batch[SampleBatch.UNROLL_ID] = np.repeat( SampleBatchBuilder._next_unroll_id, batch.count) SampleBatchBuilder._next_unroll_id += 1 self.buffers.clear() self.count = 0 return batch
# Deprecated class: Use a child class of `SampleCollector` instead # (which handles multi-agent setups as well).
[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: Dict[PolicyID, Policy], clip_rewards: bool, callbacks: "DefaultCallbacks"): """Initialize a MultiAgentSampleBatchBuilder. Args: policy_map (Dict[str,Policy]): Maps policy ids to policy instances. clip_rewards (Union[bool,float]): Whether to clip rewards before postprocessing (at +/-1.0) or the actual value to +/- clip. callbacks (DefaultCallbacks): RLlib callbacks. """ if log_once("MultiAgentSampleBatchBuilder"): deprecation_warning( old="MultiAgentSampleBatchBuilder", error=False) self.policy_map = policy_map self.clip_rewards = clip_rewards # Build the Policies' SampleBatchBuilders. self.policy_builders = { k: SampleBatchBuilder() for k in policy_map.keys() } # Whenever we observe a new agent, add a new SampleBatchBuilder for # this agent. self.agent_builders = {} # Internal agent-to-policy map. self.agent_to_policy = {} self.callbacks = callbacks # Number of "inference" steps taken in the environment. # Regardless of the number of agents involved in each of these steps. self.count = 0
[docs] def total(self) -> int: """Returns the total number of steps taken in the env (all agents). Returns: int: The number of steps taken in total in the environment over all agents. """ return sum(a.count for a in self.agent_builders.values())
[docs] def has_pending_agent_data(self) -> bool: """Returns whether there is pending unprocessed data. Returns: bool: True if there is at least one per-agent builder (with data in it). """ return len(self.agent_builders) > 0
[docs] @DeveloperAPI def add_values(self, agent_id: AgentID, policy_id: AgentID, **values: Any) -> None: """Add the given dictionary (row) of values to this batch. Args: 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 # Include the current agent id for multi-agent algorithms. if agent_id != _DUMMY_AGENT_ID: values["agent_id"] = agent_id self.agent_builders[agent_id].add_values(**values)
[docs] def postprocess_batch_so_far( self, episode: Optional[MultiAgentEpisode] = None) -> None: """Apply policy postprocessors to any unprocessed rows. This pushes the postprocessed per-agent batches onto the per-policy builders, clearing per-agent state. Args: episode (Optional[MultiAgentEpisode]): The Episode object that holds this MultiAgentBatchBuilder object. """ # 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 is True: for _, (_, pre_batch) in pre_batches.items(): pre_batch["rewards"] = np.sign(pre_batch["rewards"]) elif self.clip_rewards: for _, (_, pre_batch) in pre_batches.items(): pre_batch["rewards"] = np.clip( pre_batch["rewards"], a_min=-self.clip_rewards, a_max=self.clip_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) # Call the Policy's Exploration's postprocess method. post_batches[agent_id] = pre_batch if getattr(policy, "exploration", None) is not None: policy.exploration.postprocess_trajectory( policy, post_batches[agent_id], getattr(policy, "_sess", None)) post_batches[agent_id] = policy.postprocess_trajectory( post_batches[agent_id], other_batches, episode) if log_once("after_post"): "Trajectory fragment after postprocess_trajectory():\n\n{}\n". format(summarize(post_batches))) # Append into policy batches and reset from ray.rllib.evaluation.rollout_worker import get_global_worker for agent_id, post_batch in sorted(post_batches.items()): self.callbacks.on_postprocess_trajectory( worker=get_global_worker(), episode=episode, agent_id=agent_id, policy_id=self.agent_to_policy[agent_id], policies=self.policy_map, postprocessed_batch=post_batch, original_batches=pre_batches) self.policy_builders[self.agent_to_policy[agent_id]].add_batch( post_batch) self.agent_builders.clear() self.agent_to_policy.clear()
def check_missing_dones(self) -> None: 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: Optional[MultiAgentEpisode] = None ) -> MultiAgentBatch: """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. Args: episode (Optional[MultiAgentEpisode]): The Episode object that holds this MultiAgentBatchBuilder object or None. Returns: MultiAgentBatch: Returns the accumulated sample batches for each policy. """ 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)