Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features’ Learning Efficacy

Abstract

The focus of this study is to investigate the impact of different initialization strategies for the weight matrix of Successor Features (SF) on the learning efficiency and convergence in Reinforcement Learning (RL) agents. Using a grid-world paradigm, we compare the performance of RL agents, whose SF weight matrix is initialized with either an identity matrix, zero matrix, or a randomly generated matrix (using the Xavier, He, or uniform distribution method). Our analysis revolves around evaluating metrics such as the value error, step length, PCA of Successor Representation (SR) place field, and the distance of the SR matrices between different agents. The results demonstrate that the RL agents initialized with random matrices reach the optimal SR place field faster and showcase a quicker reduction in value error, pointing to more efficient learning. Furthermore, these random agents also exhibit a faster decrease in step length across larger grid-world environments. The study provides insights into the neurobiological interpretations of these results, their implications for understanding intelligence, and potential future research directions. These findings could have profound implications for the field of artificial intelligence, particularly in the design of learning algorithms.