Information-Theoretic Validation of Reinforcement Learning Policies

Abstract

Reinforcement learning (RL) relies heavily on reward signals to guide policy learning. However, conventional metrics like cumulative reward and average return frequently fail to capture the nuances of policy performance, particularly in complex environments. This work proposes a novel approach to validating RL policies based on information-theoretic principles. We argue that a policy's effectiveness is fundamentally tied to its ability to reduce uncertainty about the environment and maximize information gain. This paper introduces the use of metrics such as mutual information, entropy, and KL divergence to quantify the information content of state transitions and reward signals. These measures provide a more robust and interpretable assessment of policy performance compared to traditional reward-based evaluations. We demonstrate, through theoretical analysis and illustrative examples, how information-theoretic measures can effectively identify policy weaknesses, such as excessive exploration or suboptimal state selection, which are often missed by conventional metrics. The core contribution is a framework for evaluating RL policies that directly addresses the fundamental question of information flow, offering a potentially transformative shift in how we assess and compare RL algorithms.