A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm

Kwadwo Osei Bonsu

Papers from arXiv.org

Abstract: This paper proposes a geometric approach for estimating the $\alpha$ value in Q learning. We establish a systematic framework that optimizes the {\alpha} parameter, thereby enhancing learning efficiency and stability. Our results show that there is a relationship between the learning rate and the angle between a vector T (total time steps in each episode of learning) and R (the reward vector for each episode). The concept of angular bisector between vectors T and R and Nash Equilibrium provide insight into estimating $\alpha$ such that the algorithm minimizes losses arising from exploration-exploitation trade-off.

Date: 2024-08
New Economics Papers: this item is included in nep-gth
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