Uncertainty Prioritized Experience Replay

• URL: http://arxiv.org/abs/2506.09270v1
• Date: Tue, 10 Jun 2025 22:07:13 GMT
• Title: Uncertainty Prioritized Experience Replay
• Authors: Rodrigo Carrasco-Davis, Sebastian Lee, Claudia Clopath, Will Dabney,
• Abstract summary: Prioritized experience replay improves sample efficiency by selecting relevant transitions to update parameter estimates.<n>Epistemic uncertainty estimation helps to mitigate the disruptive effects of noise in value estimation.
• Score: 13.765526492965853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prioritized experience replay, which improves sample efficiency by selecting relevant transitions to update parameter estimates, is a crucial component of contemporary value-based deep reinforcement learning models. Typically, transitions are prioritized based on their temporal difference error. However, this approach is prone to favoring noisy transitions, even when the value estimation closely approximates the target mean. This phenomenon resembles the noisy TV problem postulated in the exploration literature, in which exploration-guided agents get stuck by mistaking noise for novelty. To mitigate the disruptive effects of noise in value estimation, we propose using epistemic uncertainty estimation to guide the prioritization of transitions from the replay buffer. Epistemic uncertainty quantifies the uncertainty that can be reduced by learning, hence reducing transitions sampled from the buffer generated by unpredictable random processes. We first illustrate the benefits of epistemic uncertainty prioritized replay in two tabular toy models: a simple multi-arm bandit task, and a noisy gridworld. Subsequently, we evaluate our prioritization scheme on the Atari suite, outperforming quantile regression deep Q-learning benchmarks; thus forging a path for the use of uncertainty prioritized replay in reinforcement learning agents.

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