Reliability-Adjusted Prioritized Experience Replay

• URL: http://arxiv.org/abs/2506.18482v2
• Date: Thu, 03 Jul 2025 09:55:57 GMT
• Title: Reliability-Adjusted Prioritized Experience Replay
• Authors: Leonard S. Pleiss, Tobias Sutter, Maximilian Schiffer,
• Abstract summary: We propose an extension to Prioritized Experience Replay (PER) by introducing a novel measure of temporal difference error reliability.<n>We theoretically show that the resulting transition selection algorithm, Reliability-adjusted Prioritized Experience Replay (ReaPER), enables more efficient learning than PER.
• Score: 5.342556166066767
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Experience replay enables data-efficient learning from past experiences in online reinforcement learning agents. Traditionally, experiences were sampled uniformly from a replay buffer, regardless of differences in experience-specific learning potential. In an effort to sample more efficiently, researchers introduced Prioritized Experience Replay (PER). In this paper, we propose an extension to PER by introducing a novel measure of temporal difference error reliability. We theoretically show that the resulting transition selection algorithm, Reliability-adjusted Prioritized Experience Replay (ReaPER), enables more efficient learning than PER. We further present empirical results showing that ReaPER outperforms PER across various environment types, including the Atari-10 benchmark.

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