Planning in entropy-regularized Markov decision processes and games
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914496356810752 |
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| author | Grill, Jean-Bastien Domingues, Omar Darwiche Ménard, Pierre Munos, Rémi Valko, Michal |
| author_facet | Grill, Jean-Bastien Domingues, Omar Darwiche Ménard, Pierre Munos, Rémi Valko, Michal |
| contents | We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the environment. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order O~(1/epsilon^4) for a desired accuracy epsilon, whereas for non-regularized settings there are no known algorithms with guaranteed polynomial sample complexity in the worst case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19695 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Planning in entropy-regularized Markov decision processes and games Grill, Jean-Bastien Domingues, Omar Darwiche Ménard, Pierre Munos, Rémi Valko, Michal Machine Learning We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the environment. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order O~(1/epsilon^4) for a desired accuracy epsilon, whereas for non-regularized settings there are no known algorithms with guaranteed polynomial sample complexity in the worst case. |
| title | Planning in entropy-regularized Markov decision processes and games |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.19695 |