349 citations · 900 across the 26 of their papers we have counts for
9 papers · 1 filter
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…
Improving and Benchmarking Offline Reinforcement Learning Algorithms
Bingyi Kang, Xiao Ma, Yirui Wang +2
Recently, Offline Reinforcement Learning (RL) has achieved remarkable progress with the emergence of various algorithms and datasets. However, these methods usually focus on algori…
Efficient Diffusion Policies for Offline Reinforcement Learning
Bingyi Kang, Xiao Ma, Chao Du +2
Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuio…
Boosting Offline Reinforcement Learning via Data Rebalancing
Yang Yue, Bingyi Kang, Xiao Ma +3
Offline reinforcement learning (RL) is challenged by the distributional shift between learning policies and datasets. To address this problem, existing works mainly focus on design…
Mutual Information Regularized Offline Reinforcement Learning
Xiao Ma, Bingyi Kang, Zhongwen Xu +2
The major challenge of offline RL is the distribution shift that appears when out-of-distribution actions are queried, which makes the policy improvement direction biased by extrap…
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning
Yang Yue, Bingyi Kang, Zhongwen Xu +2
Deep reinforcement learning (RL) algorithms suffer severe performance degradation when the interaction data is scarce, which limits their real-world application. Recently, visual r…
Improving Generalization in Reinforcement Learning with Mixture Regularization
Kaixin Wang, Bingyi Kang, Jie Shao +1
Deep reinforcement learning (RL) agents trained in a limited set of environments tend to suffer overfitting and fail to generalize to unseen testing environments. To improve their…
Regularization Matters in Policy Optimization
Zhuang Liu, Xuanlin Li, Bingyi Kang +1
Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regulari…
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
Tom Zahavy, Bingyi Kang, Alex Sivak +3
The question why deep learning algorithms generalize so well has attracted increasing research interest. However, most of the well-established approaches, such as hypothesis capaci…