Recurrent Off-Policy Deep Reinforcement Learning Doesn't Have to be Slow

Tyler Clark; Christine Evers; Jonathon Hare

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arXiv · 2512.20513

Recurrent Off-Policy Deep Reinforcement Learning Doesn't Have to be Slow

Tyler Clark·Christine Evers·Jonathon Hare

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Abstract

Recurrent off-policy deep reinforcement learning models achieve state-of-the-art performance but are often sidelined due to their high computational demands. In response, we introduce RISE (Recurrent Integration via Simplified Encodings), a novel approach that can leverage recurrent networks in any image-based off-policy RL setting without significant computational overheads via using both learnable and non-learnable encoder layers. When integrating RISE into leading non-recurrent off-policy RL algorithms, we observe a 35.6% human-normalized interquartile mean (IQM) performance improvement across the Atari benchmark. We analyze various implementation strategies to highlight the versatility and potential of our proposed framework.

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Tyler Clark, Christine Evers, Jonathon Hare. 2025-12-23. Recurrent Off-Policy Deep Reinforcement Learning Doesn't Have to be Slow. https://arxiv.org/abs/2512.20513

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