271 citations · 426 across the 17 of their papers we have counts for
18 papers
A Survey of Reinforcement Learning from Human Feedback
Timo Kaufmann, Paul Weng, Viktor Bengs +1
Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning (RL) that learns from human feedback instead of relying on an engineered reward function. B…
Generalization in Deep RL for TSP Problems via Equivariance and Local Search
Wenbin Ouyang, Yisen Wang, Paul Weng +1
Deep reinforcement learning (RL) has proved to be a competitive heuristic for solving small-sized instances of traveling salesman problems (TSP), but its performance on larger-size…
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards
Umer Siddique, Paul Weng, Matthieu Zimmer
As the operations of autonomous systems generally affect simultaneously several users, it is crucial that their designs account for fairness considerations. In contrast to standard…
Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation
Yijiong Lin, Jiancong Huang, Matthieu Zimmer +2
Deep reinforcement learning (DRL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. We…
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning
Yijiong Lin, Jiancong Huang, Matthieu Zimmer +3
Deep Reinforcement Learning (RL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. To a…
Fairness in Reinforcement Learning
Paul Weng
Decision support systems (e.g., for ecological conservation) and autonomous systems (e.g., adaptive controllers in smart cities) start to be deployed in real applications. Although…