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https://github.com/vale981/ray
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109 lines
3.7 KiB
Python
109 lines
3.7 KiB
Python
from typing import Any, Dict
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import random
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# Import ray before psutil will make sure we use psutil's bundled version
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import ray # noqa F401
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import psutil # noqa E402
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from ray.rllib.utils.annotations import ExperimentalAPI, override
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from ray.rllib.utils.replay_buffers.replay_buffer import ReplayBuffer
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from ray.rllib.utils.typing import SampleBatchType
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from ray.rllib.execution.buffers.replay_buffer import warn_replay_capacity
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@ExperimentalAPI
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class ReservoirBuffer(ReplayBuffer):
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"""This buffer implements reservoir sampling.
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The algorithm has been described by Jeffrey S. Vitter in "Random sampling
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with a reservoir". See https://www.cs.umd.edu/~samir/498/vitter.pdf for
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the full paper.
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"""
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def __init__(self, capacity: int = 10000, storage_unit: str = "timesteps"):
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"""Initializes a ReservoirBuffer instance.
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Args:
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capacity: Max number of timesteps to store in the FIFO
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buffer. After reaching this number, older samples will be
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dropped to make space for new ones.
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storage_unit: Either 'sequences' or 'timesteps'. Specifies how
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experiences are stored.
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"""
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ReplayBuffer.__init__(self, capacity, storage_unit)
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self._num_add_calls = 0
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self._num_evicted = 0
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@ExperimentalAPI
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@override(ReplayBuffer)
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def add(self, batch: SampleBatchType, **kwargs) -> None:
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"""Adds a batch of experiences.
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Args:
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batch: SampleBatch to add to this buffer's storage.
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"""
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# Update add counts.
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self._num_add_calls += 1
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# Update our timesteps counts.
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self._num_timesteps_added += batch.count
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self._num_timesteps_added_wrap += batch.count
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if self._num_timesteps_added < self.capacity:
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ReplayBuffer.add(self, batch)
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else:
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# Eviction of older samples has already started (buffer is "full")
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self._eviction_started = True
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idx = random.randint(0, self._num_add_calls - 1)
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if idx < self.capacity:
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self._num_evicted += 1
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self._evicted_hit_stats.push(self._hit_count[idx])
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self._hit_count[idx] = 0
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self._storage[idx] = batch
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assert batch.count > 0, batch
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warn_replay_capacity(item=batch, num_items=self.capacity / batch.count)
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@ExperimentalAPI
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@override(ReplayBuffer)
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def stats(self, debug: bool = False) -> dict:
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"""Returns the stats of this buffer.
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Args:
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debug: If True, adds sample eviction statistics to the returned
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stats dict.
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Returns:
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A dictionary of stats about this buffer.
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"""
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data = {
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"num_evicted": self._num_evicted,
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"num_add_calls": self._num_add_calls,
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}
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parent = ReplayBuffer.stats(self, debug)
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parent.update(data)
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return parent
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@ExperimentalAPI
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@override(ReplayBuffer)
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def get_state(self) -> Dict[str, Any]:
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"""Returns all local state.
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Returns:
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The serializable local state.
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"""
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parent = ReplayBuffer.get_state(self)
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parent.update(self.stats())
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return parent
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@ExperimentalAPI
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@override(ReplayBuffer)
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def set_state(self, state: Dict[str, Any]) -> None:
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"""Restores all local state to the provided `state`.
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Args:
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state: The new state to set this buffer. Can be
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obtained by calling `self.get_state()`.
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"""
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self._num_evicted = state["num_evicted"]
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self._num_add_calls = state["num_add_calls"]
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ReplayBuffer.set_state(self, state)
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