Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910313940516864 |
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| author | Yao, Zhiyuan Florescu, Ionut Lee, Chihoon |
| author_facet | Yao, Zhiyuan Florescu, Ionut Lee, Chihoon |
| contents | In this paper we are introducing a new reinforcement learning method for control problems in environments with delayed feedback. Specifically, our method employs stochastic planning, versus previous methods that used deterministic planning. This allows us to embed risk preference in the policy optimization problem. We show that this formulation can recover the optimal policy for problems with deterministic transitions. We contrast our policy with two prior methods from literature. We apply the methodology to simple tasks to understand its features. Then, we compare the performance of the methods in controlling multiple Atari games. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00313 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach Yao, Zhiyuan Florescu, Ionut Lee, Chihoon Machine Learning Systems and Control In this paper we are introducing a new reinforcement learning method for control problems in environments with delayed feedback. Specifically, our method employs stochastic planning, versus previous methods that used deterministic planning. This allows us to embed risk preference in the policy optimization problem. We show that this formulation can recover the optimal policy for problems with deterministic transitions. We contrast our policy with two prior methods from literature. We apply the methodology to simple tasks to understand its features. Then, we compare the performance of the methods in controlling multiple Atari games. |
| title | Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2402.00313 |