7 citations · 23 across the 6 of their papers we have counts for
6 papers
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…
Reinforcement Learning
Olivier Buffet, Olivier Pietquin, Paul Weng
Reinforcement learning (RL) is a general framework for adaptive control, which has proven to be efficient in many domains, e.g., board games, video games or autonomous vehicles. In…
From Preference-Based to Multiobjective Sequential Decision-Making
Paul Weng
In this paper, we present a link between preference-based and multiobjective sequential decision-making. While transforming a multiobjective problem to a preference-based one is qu…
Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs
Dajian Li, Paul Weng, Orkun Karabasoglu
In this paper, we tackle the problem of risk-averse route planning in a transportation network with time-dependent and stochastic costs. To solve this problem, we propose an adapta…
Optimizing Quantiles in Preference-based Markov Decision Processes
Hugo Gilbert, Paul Weng, Yan Xu
In the Markov decision process model, policies are usually evaluated by expected cumulative rewards. As this decision criterion is not always suitable, we propose in this paper an…
Quantile Reinforcement Learning
Hugo Gilbert, Paul Weng
In reinforcement learning, the standard criterion to evaluate policies in a state is the expectation of (discounted) sum of rewards. However, this criterion may not always be suita…