Timezone: »

Neural Logic Reinforcement Learning
zhengyao jiang · Shan Luo

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #104

Deep reinforcement learning (DRL) has achieved significant breakthroughs in various tasks. However, most DRL algorithms suffer a problem of generalising the learned policy, which makes the policy 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 showing good generalisability to environments of different initial states and problem sizes.

Author Information

zhengyao jiang (University of Liverpool)
Shan Luo (University of Liverpool)

Related Events (a corresponding poster, oral, or spotlight)