A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm
<|reference_start|>A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm: 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.<|reference_end|>
@article{bonsu2024a,
    title={A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning
  Algorithm},
    author={Kwadwo Osei Bonsu},
    journal={arXiv preprint arXiv:2408.04911},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04911},
    primaryClass={cs.LG cs.GT econ.TH math.OC}
}