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 (
_ALL_POLICIES,
ReplayBuffer,
StorageUnit,
)
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__)
@DeveloperAPI
class ReplayMode(Enum):
LOCKSTEP = "lockstep"
INDEPENDENT = "independent"
@DeveloperAPI
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)))):
logger.warning(
"Replay Buffer was initialized to have "
"underlying buffers methods called with "
"argument `{}={}`, but was subsequently called "
"with `{}={}`.".format(
arg_name,
args_on_init[arg_name],
arg_name,
arg_value,
)
)
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"):
logger.info(
"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"):
logger.info(
"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