A Survey of In-Context Reinforcement Learning
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910822456885248 |
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| author | Moeini, Amir Wang, Jiuqi Beck, Jacob Blaser, Ethan Whiteson, Shimon Chandra, Rohan Zhang, Shangtong |
| author_facet | Moeini, Amir Wang, Jiuqi Beck, Jacob Blaser, Ethan Whiteson, Shimon Chandra, Rohan Zhang, Shangtong |
| contents | Reinforcement learning (RL) agents typically optimize their policies by performing expensive backward passes to update their network parameters. However, some agents can solve new tasks without updating any parameters by simply conditioning on additional context such as their action-observation histories. This paper surveys work on such behavior, known as in-context reinforcement learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07978 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of In-Context Reinforcement Learning Moeini, Amir Wang, Jiuqi Beck, Jacob Blaser, Ethan Whiteson, Shimon Chandra, Rohan Zhang, Shangtong Machine Learning Reinforcement learning (RL) agents typically optimize their policies by performing expensive backward passes to update their network parameters. However, some agents can solve new tasks without updating any parameters by simply conditioning on additional context such as their action-observation histories. This paper surveys work on such behavior, known as in-context reinforcement learning. |
| title | A Survey of In-Context Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.07978 |