Neural+Symbolic Approaches for Interpretable Actor-Critic Reinforcement Learning

The integration of neural networks into actor-critic frameworks has been pivotal in advancing the field of reinforcement learning, enabling agents to perform complex tasks with greater efficiency and adaptability. However, neural network-based actor-critic models remain opaque ``black boxes,'' concealing their decision-making processes and hindering their use in critical applications where transparent and explainable reasoning is essential. This work introduces an innovative adaptation of the actor-critic framework that unites neural networks with rule ensembles to tackle key challenges in reinforcement learning. We harness the computational power, scalability, and adaptability of neural networks to model the critic, while integrating a rule ensemble system for the actor, ensuring transparency and interpretability for decision-making. Our study establishes a theoretical foundation for integrating rule ensembles into the Advantage Actor-Critic (A2C) framework. Experimental results from seven classic and complex environments demonstrate that our proposed method matches or exceeds the performance of representative RL models, including symbolic methods, while offering self-interpretability and transparency.