  The focus of this study is to investigate the impact of different
initialization strategies for the weight matrix of Successor Features (SF) on
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 Xavier, He, or uniform distribution method).
Our analysis revolves around evaluating metrics such as value error, step
length, PCA of Successor Representation (SR) place field, and the distance of
SR matrices between different agents. The results demonstrate that 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.
