18 citations · 75 across the 10 of their papers we have counts for
10 papers
Learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement Learning
Junqi Qian, Paul Weng, Chenmien Tan
When applying reinforcement learning (RL) to a new problem, reward engineering is a necessary, but often difficult and error-prone task a system designer has to face. To avoid this…
Neuro-Symbolic Hierarchical Rule Induction
Claire Glanois, Xuening Feng, Zhaohui Jiang +4
We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a set of meta-rules organised in…
New physics in semileptonic transitions: rare hyperon vs. kaon decays
Li-Sheng Geng, Jorge Martin Camalich, Rui-Xiang Shi
We investigate the potential of rare hyperon decays to probe the short distance structure in the and transitions. Hyperon decays into neutrinos…
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers
Wenbin Ouyang, Yisen Wang, Shaochen Han +2
Recent work applying deep reinforcement learning (DRL) to solve traveling salesman problems (TSP) has shown that DRL-based solvers can be fast and competitive with TSP heuristics f…
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning
Zhihao Ma, Yuzheng Zhuang, Paul Weng +4
Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks. However, this black-box approach fails to explain the learned policy i…
Safe Distributional Reinforcement Learning
Jianyi Zhang, Paul Weng
Safety in reinforcement learning (RL) is a key property in both training and execution in many domains such as autonomous driving or finance. In this paper, we formalize it with a…