State-free Reinforcement Learning
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910622792286208 |
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| author | Chen, Mingyu Pacchiano, Aldo Zhang, Xuezhou |
| author_facet | Chen, Mingyu Pacchiano, Aldo Zhang, Xuezhou |
| contents | In this work, we study the \textit{state-free RL} problem, where the algorithm does not have the states information before interacting with the environment. Specifically, denote the reachable state set by ${S}^Π:= \{ s|\max_{π\in Π}q^{P, π}(s)>0 \}$, we design an algorithm which requires no information on the state space $S$ while having a regret that is completely independent of ${S}$ and only depend on ${S}^Π$. We view this as a concrete first step towards \textit{parameter-free RL}, with the goal of designing RL algorithms that require no hyper-parameter tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18439 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | State-free Reinforcement Learning Chen, Mingyu Pacchiano, Aldo Zhang, Xuezhou Machine Learning Artificial Intelligence In this work, we study the \textit{state-free RL} problem, where the algorithm does not have the states information before interacting with the environment. Specifically, denote the reachable state set by ${S}^Π:= \{ s|\max_{π\in Π}q^{P, π}(s)>0 \}$, we design an algorithm which requires no information on the state space $S$ while having a regret that is completely independent of ${S}$ and only depend on ${S}^Π$. We view this as a concrete first step towards \textit{parameter-free RL}, with the goal of designing RL algorithms that require no hyper-parameter tuning. |
| title | State-free Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2409.18439 |