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Evidence record 4572 · automatically gathered

When Does Non-Uniform Replay Matter in Reinforcement Learning?

Modern off-policy reinforcement learning algorithms often rely on simple uniform replay sampling and it remains unclear when and why non-uniform replay improves over this strong baseline. Across diverse RL settings, we show that the effectiveness of non-uniform replay is governed by three factors: replay volume, the number of replayed transitions per environment step; expected recency, how recent sampled transitions are; and the entropy of the replay sampling distribution. Our main contribution

Record details

Published: 11 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (11 May 2026), “When Does Non-Uniform Replay Matter in Reinforcement Learning?,” evidence record 4572, https://ethics.ai/record/4572 (originally published by arXiv).

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