Despite numerous successes in Deep Reinforcement Learning (DRL), the learned policies are not interpretable. Moreover, since DRL does not exploit symbolic relational representations, it has difficulties in coping with structural changes in its environment (such as increasing the number of objects). Relational Reinforcement Learning, on the other hand, inherits the relational representations from symbolic planning to learn reusable policies. However, it has so far been unable to scale up and exploit the power of deep neural networks. We propose Deep Explainable Relational Reinforcement Learning (DERRL), a framework that exploits the best of both -- neural and symbolic worlds. By resorting to a neuro-symbolic approach, DERRL combines relational representations and constraints from symbolic planning with deep learning to extract interpretable policies. These policies are in the form of logical rules that explain how each decision (or action) is arrived at. Through several experiments, in setups like the Countdown Game, Blocks World, Gridworld, and Traffic, we show that the policies learned by DERRL can be applied to different configurations and contexts, hence generalizing to environmental modifications.

翻译：尽管深度强化学习（DRL）取得了诸多成功，但所学策略并不具有可解释性。此外，由于DRL未利用符号关系表示，它在应对环境结构变化（如物体数量增加）时面临困难。而关系强化学习则继承了符号规划中的关系表示，以学习可复用的策略。然而，它目前尚无法扩展并利用深度神经网络的能力。我们提出深度可解释关系强化学习（DERRL），这是一种融合了神经与符号世界优势的框架。通过采用神经符号方法，DERRL将关系表示与符号规划中的约束同深度学习相结合，以提取可解释的策略。这些策略以逻辑规则的形式呈现，阐释了每个决策（或动作）是如何得出的。通过在倒计时游戏、积木世界、网格世界和交通等多个场景的实验，我们证明了DERRL所学策略可应用于不同配置和情境，从而泛化至环境变化。