Deep reinforcement learning (DRL) has achieved significant breakthroughs in
various tasks. However, most DRL algorithms suffer a problem of generalizing
the learned policy which makes the learning performance largely affected even
by minor modifications of the training environment. Except that, the use of
deep neural networks makes the learned policies hard to be interpretable. To
address these two challenges, we propose a novel algorithm named Neural Logic
Reinforcement Learning (NLRL) to represent the policies in reinforcement
learning by first-order logic. NLRL is based on policy gradient methods and
differentiable inductive logic programming that have demonstrated significant
advantages in terms of interpretability and generalisability in supervised
tasks. Extensive experiments conducted on cliff-walking and blocks manipulation
tasks demonstrate that NLRL can induce interpretable policies achieving
near-optimal performance while demonstrating good generalisability to
environments of different initial states and problem sizes.

本文提出了一种名为神经逻辑强化学习（NLRL）的新算法，基于策略梯度方法和可微分归纳逻辑编程，通过一阶逻辑来表示强化学习中的策略，解决了深度神经网络难以解释和学习泛化能力低的问题。实验表明，该算法可以在不同的环境下归纳出解释性强且性能接近最优的策略。