ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer
ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer#
- class ray.rllib.utils.replay_buffers.reservoir_replay_buffer.ReservoirReplayBuffer(capacity: int = 10000, storage_unit: str = 'timesteps', **kwargs)[source]#
Bases:
ray.rllib.utils.replay_buffers.replay_buffer.ReplayBuffer
This buffer implements reservoir sampling.
The algorithm has been described by Jeffrey S. Vitter in “Random sampling with a reservoir”.
Methods
__init__
([capacity, storage_unit])Initializes a ReservoirBuffer instance.
add
(batch, **kwargs)Adds a batch of experiences or other data to this buffer.
apply
(func, *args, **kwargs)Calls the given function with this rollout worker instance.
get_host
()Returns the computer's network name.
Returns all local state.
ping
()Ping the actor.
sample
([num_items])Samples
num_items
items from this buffer.set_state
(state)Restores all local state to the provided
state
.stats
([debug])Returns the stats of this buffer.