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arXiv · 2306.15029

Beyond dynamic programming

Abhinav Muraleedharan
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Abstract

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.

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Abhinav Muraleedharan. 2023-06-26. Beyond dynamic programming. https://arxiv.org/abs/2306.15029

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