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Reverb: A Framework For Experience Replay
[article]
2021
arXiv
pre-print
A central component of training in Reinforcement Learning (RL) is Experience: the data used for training. The mechanisms used to generate and consume this data have an important effect on the performance of RL algorithms. In this paper, we introduce Reverb: an efficient, extensible, and easy to use system designed specifically for experience replay in RL. Reverb is designed to work efficiently in distributed configurations with up to thousands of concurrent clients. The flexible API provides
arXiv:2102.04736v1
fatcat:ngrmmrsv2vcivdvvgqxjx5mufi