MDP Geometry, Normalization and Reward Balancing Solvers
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912259205234688 |
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| author | Mustafin, Arsenii Pakharev, Aleksei Olshevsky, Alex Paschalidis, Ioannis Ch. |
| author_facet | Mustafin, Arsenii Pakharev, Aleksei Olshevsky, Alex Paschalidis, Ioannis Ch. |
| contents | We present a new geometric interpretation of Markov Decision Processes (MDPs) with a natural normalization procedure that allows us to adjust the value function at each state without altering the advantage of any action with respect to any policy. This advantage-preserving transformation of the MDP motivates a class of algorithms which we call Reward Balancing, which solve MDPs by iterating through these transformations, until an approximately optimal policy can be trivially found. We provide a convergence analysis of several algorithms in this class, in particular showing that for MDPs for unknown transition probabilities we can improve upon state-of-the-art sample complexity results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06712 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MDP Geometry, Normalization and Reward Balancing Solvers Mustafin, Arsenii Pakharev, Aleksei Olshevsky, Alex Paschalidis, Ioannis Ch. Machine Learning Optimization and Control We present a new geometric interpretation of Markov Decision Processes (MDPs) with a natural normalization procedure that allows us to adjust the value function at each state without altering the advantage of any action with respect to any policy. This advantage-preserving transformation of the MDP motivates a class of algorithms which we call Reward Balancing, which solve MDPs by iterating through these transformations, until an approximately optimal policy can be trivially found. We provide a convergence analysis of several algorithms in this class, in particular showing that for MDPs for unknown transition probabilities we can improve upon state-of-the-art sample complexity results. |
| title | MDP Geometry, Normalization and Reward Balancing Solvers |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2407.06712 |