A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm
This work addresses a specific bottleneck in reinforcement learning for practitioners, but it appears incremental as it builds on existing Q-learning methods.
The paper tackles the problem of tuning the learning rate in Q-learning by proposing a geometric approach that relates it to the angle between time steps and reward vectors, resulting in enhanced learning efficiency and stability.
This paper proposes a geometric approach for estimating the $α$ value in Q learning. We establish a systematic framework that optimizes the α 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 $α$ such that the algorithm minimizes losses arising from exploration-exploitation trade-off.