4 papers
Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning
Liyuan Mao, Haoran Xu, Xianyuan Zhan +2
One important property of DIstribution Correction Estimation (DICE) methods is that the solution is the optimal stationary distribution ratio between the optimized and data collect…
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
Liyuan Mao, Haoran Xu, Weinan Zhang +1
In this study, we investigate the DIstribution Correction Estimation (DICE) methods, an important line of work in offline reinforcement learning (RL) and imitation learning (IL). D…
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
Xiangsen Wang, Haoran Xu, Yinan Zheng +1
Offline reinforcement learning (RL) has received considerable attention in recent years due to its attractive capability of learning policies from offline datasets without environm…
Offline Reinforcement Learning with Imbalanced Datasets
Li Jiang, Sijie Cheng, Jielin Qiu +3
The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development…