Source code for ray.rllib.utils.replay_buffers.multi_agent_replay_buffer

import collections
import logging
from enum import Enum
from typing import Any, Dict, Optional

from ray.util.timer import _Timer
from ray.rllib.policy.rnn_sequencing import timeslice_along_seq_lens_with_overlap
from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch
from ray.rllib.utils.annotations import override
from ray.rllib.utils.deprecation import Deprecated
from ray.rllib.utils.from_config import from_config
from ray.rllib.utils.replay_buffers.replay_buffer import (
from ray.rllib.utils.typing import PolicyID, SampleBatchType
from ray.util.annotations import DeveloperAPI
from ray.util.debug import log_once

logger = logging.getLogger(__name__)

class ReplayMode(Enum):
    LOCKSTEP = "lockstep"
    INDEPENDENT = "independent"

def merge_dicts_with_warning(args_on_init, args_on_call):
    """Merge argument dicts, overwriting args_on_call with warning.

    The MultiAgentReplayBuffer supports setting standard arguments for calls
    of methods of the underlying buffers. These arguments can be
    overwritten. Such overwrites trigger a warning to the user.
    for arg_name, arg_value in args_on_call.items():
        if arg_name in args_on_init:
            if log_once("overwrite_argument_{}".format((str(arg_name)))):
                    "Replay Buffer was initialized to have "
                    "underlying buffers methods called with "
                    "argument `{}={}`, but was subsequently called "
                    "with `{}={}`.".format(
    return {**args_on_init, **args_on_call}

[docs]@DeveloperAPI class MultiAgentReplayBuffer(ReplayBuffer): """A replay buffer shard for multiagent setups. This buffer is meant to be run in parallel to distribute experiences across `num_shards` shards. Unlike simpler buffers, it holds a set of buffers - one for each policy ID. """
[docs] def __init__( self, capacity: int = 10000, storage_unit: str = "timesteps", num_shards: int = 1, replay_mode: str = "independent", replay_sequence_override: bool = True, replay_sequence_length: int = 1, replay_burn_in: int = 0, replay_zero_init_states: bool = True, underlying_buffer_config: dict = None, **kwargs ): """Initializes a MultiAgentReplayBuffer instance. Args: capacity: The capacity of the buffer, measured in `storage_unit`. storage_unit: Either 'timesteps', 'sequences' or 'episodes'. Specifies how experiences are stored. If they are stored in episodes, replay_sequence_length is ignored. num_shards: The number of buffer shards that exist in total (including this one). replay_mode: One of "independent" or "lockstep". Determines, whether batches are sampled independently or to an equal amount. replay_sequence_override: If True, ignore sequences found in incoming batches, slicing them into sequences as specified by `replay_sequence_length` and `replay_sequence_burn_in`. This only has an effect if storage_unit is `sequences`. replay_sequence_length: The sequence length (T) of a single sample. If > 1, we will sample B x T from this buffer. This only has an effect if storage_unit is 'timesteps'. replay_burn_in: This is the number of timesteps each sequence overlaps with the previous one to generate a better internal state (=state after the burn-in), instead of starting from 0.0 each RNN rollout. This only has an effect if storage_unit is `sequences`. replay_zero_init_states: Whether the initial states in the buffer (if replay_sequence_length > 0) are alwayas 0.0 or should be updated with the previous train_batch state outputs. underlying_buffer_config: A config that contains all necessary constructor arguments and arguments for methods to call on the underlying buffers. ``**kwargs``: Forward compatibility kwargs. """ shard_capacity = capacity // num_shards ReplayBuffer.__init__(self, capacity, storage_unit) # If the user provides an underlying buffer config, we use to # instantiate and interact with underlying buffers self.underlying_buffer_config = underlying_buffer_config if self.underlying_buffer_config is not None: self.underlying_buffer_call_args = self.underlying_buffer_config else: self.underlying_buffer_call_args = {} self.replay_sequence_override = replay_sequence_override self.replay_mode = replay_mode self.replay_sequence_length = replay_sequence_length self.replay_burn_in = replay_burn_in self.replay_zero_init_states = replay_zero_init_states self.replay_sequence_override = replay_sequence_override if ( replay_sequence_length > 1 and self.storage_unit is not StorageUnit.SEQUENCES ): logger.warning( "MultiAgentReplayBuffer configured with " "`replay_sequence_length={}`, but `storage_unit={}`. " "replay_sequence_length will be ignored and set to 1.".format( replay_sequence_length, storage_unit ) ) self.replay_sequence_length = 1 if replay_sequence_length == 1 and self.storage_unit is StorageUnit.SEQUENCES: logger.warning( "MultiAgentReplayBuffer configured with " "`replay_sequence_length={}`, but `storage_unit={}`. " "This will result in sequences equal to timesteps.".format( replay_sequence_length, storage_unit ) ) if replay_mode in ["lockstep", ReplayMode.LOCKSTEP]: self.replay_mode = ReplayMode.LOCKSTEP if self.storage_unit in [StorageUnit.EPISODES, StorageUnit.SEQUENCES]: raise ValueError( "MultiAgentReplayBuffer does not support " "lockstep mode with storage unit `episodes`" "or `sequences`." ) elif replay_mode in ["independent", ReplayMode.INDEPENDENT]: self.replay_mode = ReplayMode.INDEPENDENT else: raise ValueError("Unsupported replay mode: {}".format(replay_mode)) if self.underlying_buffer_config: ctor_args = { **{"capacity": shard_capacity, "storage_unit": StorageUnit.FRAGMENTS}, **self.underlying_buffer_config, } def new_buffer(): return from_config(self.underlying_buffer_config["type"], ctor_args) else: # Default case def new_buffer(): self.underlying_buffer_call_args = {} return ReplayBuffer( self.capacity, storage_unit=StorageUnit.FRAGMENTS, ) self.replay_buffers = collections.defaultdict(new_buffer) # Metrics. self.add_batch_timer = _Timer() self.replay_timer = _Timer() self._num_added = 0
def __len__(self) -> int: """Returns the number of items currently stored in this buffer.""" return sum(len(buffer._storage) for buffer in self.replay_buffers.values()) @DeveloperAPI @Deprecated( old="ReplayBuffer.replay()", new="ReplayBuffer.sample(num_items)", error=True, ) def replay(self, num_items: int = None, **kwargs) -> Optional[SampleBatchType]: """Deprecated in favor of new ReplayBuffer API.""" pass
[docs] @DeveloperAPI @override(ReplayBuffer) def add(self, batch: SampleBatchType, **kwargs) -> None: """Adds a batch to the appropriate policy's replay buffer. Turns the batch into a MultiAgentBatch of the DEFAULT_POLICY_ID if it is not a MultiAgentBatch. Subsequently, adds the individual policy batches to the storage. Args: batch : The batch to be added. ``**kwargs``: Forward compatibility kwargs. """ if batch is None: if log_once("empty_batch_added_to_buffer"): "A batch that is `None` was added to {}. This can be " "normal at the beginning of execution but might " "indicate an issue.".format(type(self).__name__) ) return # Make a copy so the replay buffer doesn't pin plasma memory. batch = batch.copy() # Handle everything as if multi-agent. batch = batch.as_multi_agent() with self.add_batch_timer: pids_and_batches = self._maybe_split_into_policy_batches(batch) for policy_id, sample_batch in pids_and_batches.items(): self._add_to_underlying_buffer(policy_id, sample_batch, **kwargs) self._num_added += batch.count
@DeveloperAPI def _add_to_underlying_buffer( self, policy_id: PolicyID, batch: SampleBatchType, **kwargs ) -> None: """Add a batch of experiences to the underlying buffer of a policy. If the storage unit is `timesteps`, cut the batch into timeslices before adding them to the appropriate buffer. Otherwise, let the underlying buffer decide how slice batches. Args: policy_id: ID of the policy that corresponds to the underlying buffer batch: SampleBatch to add to the underlying buffer ``**kwargs``: Forward compatibility kwargs. """ # Merge kwargs, overwriting standard call arguments kwargs = merge_dicts_with_warning(self.underlying_buffer_call_args, kwargs) # For the storage unit `timesteps`, the underlying buffer will # simply store the samples how they arrive. For sequences and # episodes, the underlying buffer may split them itself. if self.storage_unit is StorageUnit.TIMESTEPS: timeslices = batch.timeslices(1) elif self.storage_unit is StorageUnit.SEQUENCES: timeslices = timeslice_along_seq_lens_with_overlap( sample_batch=batch, seq_lens=batch.get(SampleBatch.SEQ_LENS) if self.replay_sequence_override else None, zero_pad_max_seq_len=self.replay_sequence_length, pre_overlap=self.replay_burn_in, zero_init_states=self.replay_zero_init_states, ) elif self.storage_unit == StorageUnit.EPISODES: timeslices = [] for eps in batch.split_by_episode(): if eps.get(SampleBatch.T)[0] == 0 and ( eps.get(SampleBatch.TERMINATEDS, [True])[-1] or eps.get(SampleBatch.TRUNCATEDS, [False])[-1] ): # Only add full episodes to the buffer timeslices.append(eps) else: if log_once("only_full_episodes"): "This buffer uses episodes as a storage " "unit and thus allows only full episodes " "to be added to it. Some samples may be " "dropped." ) elif self.storage_unit == StorageUnit.FRAGMENTS: timeslices = [batch] else: raise ValueError("Unknown `storage_unit={}`".format(self.storage_unit)) for slice in timeslices: self.replay_buffers[policy_id].add(slice, **kwargs)
[docs] @DeveloperAPI @override(ReplayBuffer) def sample( self, num_items: int, policy_id: Optional[PolicyID] = None, **kwargs ) -> Optional[SampleBatchType]: """Samples a MultiAgentBatch of `num_items` per one policy's buffer. If less than `num_items` records are in the policy's buffer, some samples in the results may be repeated to fulfil the batch size `num_items` request. Returns an empty batch if there are no items in the buffer. Args: num_items: Number of items to sample from a policy's buffer. policy_id: ID of the policy that created the experiences we sample. If none is given, sample from all policies. Returns: Concatenated MultiAgentBatch of items. ``**kwargs``: Forward compatibility kwargs. """ # Merge kwargs, overwriting standard call arguments kwargs = merge_dicts_with_warning(self.underlying_buffer_call_args, kwargs) with self.replay_timer: # Lockstep mode: Sample from all policies at the same time an # equal amount of steps. if self.replay_mode == ReplayMode.LOCKSTEP: assert ( policy_id is None ), "`policy_id` specifier not allowed in `lockstep` mode!" # In lockstep mode we sample MultiAgentBatches return self.replay_buffers[_ALL_POLICIES].sample(num_items, **kwargs) elif policy_id is not None: sample = self.replay_buffers[policy_id].sample(num_items, **kwargs) return MultiAgentBatch({policy_id: sample}, sample.count) else: samples = {} for policy_id, replay_buffer in self.replay_buffers.items(): samples[policy_id] = replay_buffer.sample(num_items, **kwargs) return MultiAgentBatch(samples, sum(s.count for s in samples.values()))
[docs] @DeveloperAPI @override(ReplayBuffer) def stats(self, debug: bool = False) -> Dict: """Returns the stats of this buffer and all underlying buffers. Args: debug: If True, stats of underlying replay buffers will be fetched with debug=True. Returns: stat: Dictionary of buffer stats. """ stat = { "add_batch_time_ms": round(1000 * self.add_batch_timer.mean, 3), "replay_time_ms": round(1000 * self.replay_timer.mean, 3), } for policy_id, replay_buffer in self.replay_buffers.items(): stat.update( {"policy_{}".format(policy_id): replay_buffer.stats(debug=debug)} ) return stat
[docs] @DeveloperAPI @override(ReplayBuffer) def get_state(self) -> Dict[str, Any]: """Returns all local state. Returns: The serializable local state. """ state = {"num_added": self._num_added, "replay_buffers": {}} for policy_id, replay_buffer in self.replay_buffers.items(): state["replay_buffers"][policy_id] = replay_buffer.get_state() return state
[docs] @DeveloperAPI @override(ReplayBuffer) def set_state(self, state: Dict[str, Any]) -> None: """Restores all local state to the provided `state`. Args: state: The new state to set this buffer. Can be obtained by calling `self.get_state()`. """ self._num_added = state["num_added"] buffer_states = state["replay_buffers"] for policy_id in buffer_states.keys(): self.replay_buffers[policy_id].set_state(buffer_states[policy_id])
def _maybe_split_into_policy_batches(self, batch: SampleBatchType): """Returns a dict of policy IDs and batches, depending on our replay mode. This method helps with splitting up MultiAgentBatches only if the self.replay_mode requires it. """ if self.replay_mode == ReplayMode.LOCKSTEP: return {_ALL_POLICIES: batch} else: return batch.policy_batches