Explainable Reinforcement Learning with Causal Inference

Abstract

This paper explores a novel approach to reinforcement learning (RL) that integrates causal inference to enhance both the reliability and interpretability of decision-making processes. Traditional RL algorithms often operate as "black boxes," lacking transparency into the reasoning behind their actions. This research addresses this limitation by leveraging causal relationships within an environment to guide the learning process. We propose a framework where causal graphs are utilized to understand the underlying dynamics of the environment, informing the selection of optimal policies in an RL agent. Furthermore, we employ explainable AI (XAI) techniques to provide insights into the agent's decision-making rationale. The core claim is that by combining causal reasoning with RL and XAI, we can significantly improve the robustness and trustworthiness of the learned policies, while simultaneously increasing their interpretability. The proposed method aims to provide a more reliable and understandable system than standard RL approaches, particularly in complex and uncertain environments.