Explainable AI for Reinforcement Learning through Causal Reasoning

Abstract

Reinforcement learning (RL) has achieved remarkable success in various domains, from game playing to robotics. However, the "black box" nature of many RL agents presents a significant challenge, particularly in applications where transparency and trust are paramount. This work proposes a novel approach to explainable AI (XAI) within the context of RL by integrating causal reasoning. Traditional RL methods primarily rely on correlation-based learning, which can lead to spurious correlations and opaque decision-making processes. Our approach introduces a mechanism where RL agents explicitly identify and reason about causal relationships between states, actions, and rewards. This allows the agent to articulate *why* it took a particular action, connecting it to the underlying causes of the situation. We formalize this process using a causal Bayesian network and demonstrate how this framework can be implemented within a standard RL architecture. The core contribution is a method for generating explanations that are not only plausible but also reflect a genuine understanding of the environment's causal structure. This framework offers a path towards more robust, trustworthy, and interpretable RL systems, addressing a critical limitation of current approaches.