most citedAny-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.LG2024★ 1 cited

Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning

Haoxin Lin, Yu-Yan Xu, Yihao Sun +6

Model-based methods in reinforcement learning offer a promising approach to enhance data efficiency by facilitating policy exploration within a dynamics model. However, accurately…

cs.LG2023

Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments

Xiong-Hui Chen, Junyin Ye, Hang Zhao +9

Imitation learning (IL) enables agents to mimic expert behaviors. Most previous IL techniques focus on precisely imitating one policy through mass demonstrations. However, in many…