Beyond dynamic programming
| Autor: | Muraleedharan, Abhinav |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | In this paper, we present Score-life programming, a novel theoretical approach for solving reinforcement learning problems. In contrast with classical dynamic programming-based methods, our method can search over non-stationary policy functions, and can directly compute optimal infinite horizon action sequences from a given state. The central idea in our method is the construction of a mapping between infinite horizon action sequences and real numbers in a bounded interval. This construction enables us to formulate an optimization problem for directly computing optimal infinite horizon action sequences, without requiring a policy function. We demonstrate the effectiveness of our approach by applying it to nonlinear optimal control problems. Overall, our contributions provide a novel theoretical framework for formulating and solving reinforcement learning problems. Comment: 17 pages. Colab Notebook: https://colab.research.google.com/drive/1GKIMieKrYLX_YXnUOFuEvHwk8CH26zVu?usp=sharing github repo/code: https://github.com/Abhinav-Muraleedharan/Beyond_Dynamic_Programming.git |
| Databáze: | arXiv |
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