import random
from typing import Any, Dict, List, Optional
import numpy as np
# Import ray before psutil will make sure we use psutil's bundled version
import ray # noqa F401
import psutil # noqa E402
from ray.rllib.execution.segment_tree import SumSegmentTree, MinSegmentTree
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.annotations import override
from ray.rllib.utils.metrics.window_stat import WindowStat
from ray.rllib.utils.replay_buffers.replay_buffer import ReplayBuffer
from ray.rllib.utils.typing import SampleBatchType
from ray.util.annotations import DeveloperAPI
[docs]@DeveloperAPI
class PrioritizedReplayBuffer(ReplayBuffer):
"""This buffer implements Prioritized Experience Replay.
The algorithm has been described by Tom Schaul et. al. in "Prioritized
Experience Replay". See https://arxiv.org/pdf/1511.05952.pdf for
the full paper.
"""
[docs] def __init__(
self,
capacity: int = 10000,
storage_unit: str = "timesteps",
alpha: float = 1.0,
**kwargs
):
"""Initializes a PrioritizedReplayBuffer instance.
Args:
capacity: Max number of timesteps to store in the FIFO
buffer. After reaching this number, older samples will be
dropped to make space for new ones.
storage_unit: Either 'timesteps', 'sequences' or
'episodes'. Specifies how experiences are stored.
alpha: How much prioritization is used
(0.0=no prioritization, 1.0=full prioritization).
``**kwargs``: Forward compatibility kwargs.
"""
ReplayBuffer.__init__(self, capacity, storage_unit, **kwargs)
assert alpha > 0
self._alpha = alpha
# Segment tree must have capacity that is a power of 2
it_capacity = 1
while it_capacity < self.capacity:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
self._prio_change_stats = WindowStat("reprio", 1000)
@DeveloperAPI
@override(ReplayBuffer)
def _add_single_batch(self, item: SampleBatchType, **kwargs) -> None:
"""Add a batch of experiences to self._storage with weight.
An item consists of either one or more timesteps, a sequence or an
episode. Differs from add() in that it does not consider the storage
unit or type of batch and simply stores it.
Args:
item: The item to be added.
``**kwargs``: Forward compatibility kwargs.
"""
weight = kwargs.get("weight", None)
if weight is None:
weight = self._max_priority
self._it_sum[self._next_idx] = weight**self._alpha
self._it_min[self._next_idx] = weight**self._alpha
ReplayBuffer._add_single_batch(self, item)
def _sample_proportional(self, num_items: int) -> List[int]:
res = []
for _ in range(num_items):
# TODO(szymon): should we ensure no repeats?
mass = random.random() * self._it_sum.sum(0, len(self._storage))
idx = self._it_sum.find_prefixsum_idx(mass)
res.append(idx)
return res
[docs] @DeveloperAPI
@override(ReplayBuffer)
def sample(
self, num_items: int, beta: float, **kwargs
) -> Optional[SampleBatchType]:
"""Sample `num_items` items from this buffer, including prio. weights.
Samples in the results may be repeated.
Examples for storage of SamplesBatches:
- If storage unit `timesteps` has been chosen and batches of
size 5 have been added, sample(5) will yield a concatenated batch of
15 timesteps.
- If storage unit 'sequences' has been chosen and sequences of
different lengths have been added, sample(5) will yield a concatenated
batch with a number of timesteps equal to the sum of timesteps in
the 5 sampled sequences.
- If storage unit 'episodes' has been chosen and episodes of
different lengths have been added, sample(5) will yield a concatenated
batch with a number of timesteps equal to the sum of timesteps in
the 5 sampled episodes.
Args:
num_items: Number of items to sample from this buffer.
beta: To what degree to use importance weights (0 - no corrections,
1 - full correction).
``**kwargs``: Forward compatibility kwargs.
Returns:
Concatenated SampleBatch of items including "weights" and
"batch_indexes" fields denoting IS of each sampled
transition and original idxes in buffer of sampled experiences.
"""
assert beta >= 0.0
if len(self) == 0:
raise ValueError("Trying to sample from an empty buffer.")
idxes = self._sample_proportional(num_items)
weights = []
batch_indexes = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self)) ** (-beta)
for idx in idxes:
p_sample = self._it_sum[idx] / self._it_sum.sum()
weight = (p_sample * len(self)) ** (-beta)
count = self._storage[idx].count
# If zero-padded, count will not be the actual batch size of the
# data.
if (
isinstance(self._storage[idx], SampleBatch)
and self._storage[idx].zero_padded
):
actual_size = self._storage[idx].max_seq_len
else:
actual_size = count
weights.extend([weight / max_weight] * actual_size)
batch_indexes.extend([idx] * actual_size)
self._num_timesteps_sampled += count
batch = self._encode_sample(idxes)
# Note: prioritization is not supported in multi agent lockstep
if isinstance(batch, SampleBatch):
batch["weights"] = np.array(weights)
batch["batch_indexes"] = np.array(batch_indexes)
return batch
[docs] @DeveloperAPI
def update_priorities(self, idxes: List[int], priorities: List[float]) -> None:
"""Update priorities of items at given indices.
Sets priority of item at index idxes[i] in buffer
to priorities[i].
Args:
idxes: List of indices of items
priorities: List of updated priorities corresponding to items at the
idxes denoted by variable `idxes`.
"""
# Making sure we don't pass in e.g. a torch tensor.
assert isinstance(
idxes, (list, np.ndarray)
), "ERROR: `idxes` is not a list or np.ndarray, but {}!".format(
type(idxes).__name__
)
assert len(idxes) == len(priorities)
for idx, priority in zip(idxes, priorities):
assert priority > 0
assert 0 <= idx < len(self._storage)
delta = priority**self._alpha - self._it_sum[idx]
self._prio_change_stats.push(delta)
self._it_sum[idx] = priority**self._alpha
self._it_min[idx] = priority**self._alpha
self._max_priority = max(self._max_priority, priority)
[docs] @DeveloperAPI
@override(ReplayBuffer)
def stats(self, debug: bool = False) -> Dict:
"""Returns the stats of this buffer.
Args:
debug: If true, adds sample eviction statistics to the returned stats dict.
Returns:
A dictionary of stats about this buffer.
"""
parent = ReplayBuffer.stats(self, debug)
if debug:
parent.update(self._prio_change_stats.stats())
return parent
[docs] @DeveloperAPI
@override(ReplayBuffer)
def get_state(self) -> Dict[str, Any]:
"""Returns all local state.
Returns:
The serializable local state.
"""
# Get parent state.
state = super().get_state()
# Add prio weights.
state.update(
{
"sum_segment_tree": self._it_sum.get_state(),
"min_segment_tree": self._it_min.get_state(),
"max_priority": self._max_priority,
}
)
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()`.
"""
super().set_state(state)
self._it_sum.set_state(state["sum_segment_tree"])
self._it_min.set_state(state["min_segment_tree"])
self._max_priority = state["max_priority"]