Recurrent Off-Policy Deep Reinforcement Learning Doesn't Have to be Slow
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914217640067072 |
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| author | Clark, Tyler Evers, Christine Hare, Jonathon |
| author_facet | Clark, Tyler Evers, Christine Hare, Jonathon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20513 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recurrent Off-Policy Deep Reinforcement Learning Doesn't Have to be Slow Clark, Tyler Evers, Christine Hare, Jonathon Machine Learning 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. |
| title | Recurrent Off-Policy Deep Reinforcement Learning Doesn't Have to be Slow |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.20513 |