3 papers
cs.LG2025
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline Data
Shilong Deng, Zetao Zheng, Hongcai He +2
A major challenge in Reinforcement Learning (RL) is the difficulty of learning an optimal policy from sparse rewards. Prior works enhance online RL with conventional Imitation Lear…
cs.LG2024
Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action Ranker
Jiangdong Fan, Hongcai He, Paul Weng +2
A major bottleneck in imitation learning is the requirement of a large number of expert demonstrations, which can be expensive or inaccessible. Learning from supplementary demonstr…
cs.LG2023
Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills
Hongcai He, Anjie Zhu, Shuang Liang +2
Offline meta-reinforcement learning (meta-RL) methods, which adapt to unseen target tasks with prior experience, are essential in robot control tasks. Current methods typically uti…