Scale-free adaptive planning for deterministic dynamics & discounted rewards
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866917422865317888 |
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| author | Bartlett, Peter L. Gabillon, Victor Healey, Jennifer Valko, Michal |
| author_facet | Bartlett, Peter L. Gabillon, Victor Healey, Jennifer Valko, Michal |
| contents | We address the problem of planning in an environment with deterministic dynamics and stochastic rewards with discounted returns. The optimal value function is not known, nor are the rewards bounded. We propose Platypoos, a simple scale-free planning algorithm that adapts to the unknown scale and smoothness of the reward function. We provide a sample complexity analysis for Platypoos that improves upon prior work and holds simultaneously over a broad range of discount factors and reward scales, without the algorithm knowing them. We also establish a matching lower bound showing our analysis is optimal up to constants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18312 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scale-free adaptive planning for deterministic dynamics & discounted rewards Bartlett, Peter L. Gabillon, Victor Healey, Jennifer Valko, Michal Machine Learning We address the problem of planning in an environment with deterministic dynamics and stochastic rewards with discounted returns. The optimal value function is not known, nor are the rewards bounded. We propose Platypoos, a simple scale-free planning algorithm that adapts to the unknown scale and smoothness of the reward function. We provide a sample complexity analysis for Platypoos that improves upon prior work and holds simultaneously over a broad range of discount factors and reward scales, without the algorithm knowing them. We also establish a matching lower bound showing our analysis is optimal up to constants. |
| title | Scale-free adaptive planning for deterministic dynamics & discounted rewards |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.18312 |