271 citations · 426 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains
Matthieu Zimmer, Paul Weng
In the context of learning deterministic policies in continuous domains, we revisit an approach, which was first proposed in Continuous Actor Critic Learning Automaton (CACLA) and…
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
Qitian Wu, Hengrui Zhang, Xiaofeng Gao +4
Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assu…