Early Empirical Results on Reinforcement Symbolic Learning

Overview & Synopsis

Reinforcement learning is a subfield of machine learning that is concerned with how agents learn to make decisions in an environment in order to maximize some notion of reward. It has shown great promise in a variety of domains, but it often struggles with generalization and interpretability due to its reliance on black-box models. One approach to address this issue is to incorporate knowledge representation into reinforcement learning. In this work, we explore the benefits of using symbolic representation in reinforcement learning and present preliminary results on its performance compared to standard reinforcement learning techniques. Our experiments show that the use of symbolic representation can significantly improve the generalization capabilities of reinforcement learning agents. We also discuss the potential challenges and limitations of this approach and suggest avenues for future research.