16 citations · 19 across the 3 of their papers we have counts for
3 papers
cs.LG2022
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning
Chen Chen, Hongyao Tang, Yi Ma +4
Pessimism is of great importance in offline reinforcement learning (RL). One broad category of offline RL algorithms fulfills pessimism by explicit or implicit behavior regularizat…
cs.LG2022★ 3 cited
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu, Moksh Jain, Bonaventure Dossou +10
Bayesian Inference offers principled tools to tackle many critical problems with modern neural networks such as poor calibration and generalization, and data inefficiency. However,…
cs.LG2020★ 16 cited
Deep Reinforcement Learning with Robust and Smooth Policy
Qianli Shen, Yan Li, Haoming Jiang +2
Deep reinforcement learning (RL) has achieved great empirical successes in various domains. However, the large search space of neural networks requires a large amount of data, whic…