4 papers
Decoupled Prioritized Resampling for Offline RL
Yang Yue, Bingyi Kang, Xiao Ma +4
Offline reinforcement learning (RL) is challenged by the distributional shift problem. To address this problem, existing works mainly focus on designing sophisticated policy constr…
BAFFLE: A Baseline of Backpropagation-Free Federated Learning
Haozhe Feng, Tianyu Pang, Chao Du +3
Federated learning (FL) is a general principle for decentralized clients to train a server model collectively without sharing local data. FL is a promising framework with practical…
Learning to Optimize for Reinforcement Learning
Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan +1
In recent years, by leveraging more data, computation, and diverse tasks, learned optimizers have achieved remarkable success in supervised learning, outperforming classical hand-d…
Reinforcement Learning from Diverse Human Preferences
Wanqi Xue, Bo An, Shuicheng Yan +1
The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques. Describing an agent's desired behavio…