Off-Policy Reinforcement Learning with High Dimensional Reward
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916357394661376 |
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| author | Lee, Dong Neuck Kosorok, Michael R. |
| author_facet | Lee, Dong Neuck Kosorok, Michael R. |
| contents | Conventional off-policy reinforcement learning (RL) focuses on maximizing the expected return of scalar rewards. Distributional RL (DRL), in contrast, studies the distribution of returns with the distributional Bellman operator in a Euclidean space, leading to highly flexible choices for utility. This paper establishes robust theoretical foundations for DRL. We prove the contraction property of the Bellman operator even when the reward space is an infinite-dimensional separable Banach space. Furthermore, we demonstrate that the behavior of high- or infinite-dimensional returns can be effectively approximated using a lower-dimensional Euclidean space. Leveraging these theoretical insights, we propose a novel DRL algorithm that tackles problems which have been previously intractable using conventional reinforcement learning approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07660 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Off-Policy Reinforcement Learning with High Dimensional Reward Lee, Dong Neuck Kosorok, Michael R. Machine Learning 68T05, 46B09 (Primary) 46B06 (Secondary) Conventional off-policy reinforcement learning (RL) focuses on maximizing the expected return of scalar rewards. Distributional RL (DRL), in contrast, studies the distribution of returns with the distributional Bellman operator in a Euclidean space, leading to highly flexible choices for utility. This paper establishes robust theoretical foundations for DRL. We prove the contraction property of the Bellman operator even when the reward space is an infinite-dimensional separable Banach space. Furthermore, we demonstrate that the behavior of high- or infinite-dimensional returns can be effectively approximated using a lower-dimensional Euclidean space. Leveraging these theoretical insights, we propose a novel DRL algorithm that tackles problems which have been previously intractable using conventional reinforcement learning approaches. |
| title | Off-Policy Reinforcement Learning with High Dimensional Reward |
| topic | Machine Learning 68T05, 46B09 (Primary) 46B06 (Secondary) |
| url | https://arxiv.org/abs/2408.07660 |