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cs.LG2025
ReLAM: Learning Anticipation Model for Rewarding Visual Robotic Manipulation
Nan Tang, Jing-Cheng Pang, Guanlin Li +2
Reward design remains a critical bottleneck in visual reinforcement learning (RL) for robotic manipulation. In simulated environments, rewards are conventionally designed based on…
cs.LG2025
ImagineBench: Evaluating Reinforcement Learning with Large Language Model Rollouts
Jing-Cheng Pang, Kaiyuan Li, Yidi Wang +3
A central challenge in reinforcement learning (RL) is its dependence on extensive real-world interaction data to learn task-specific policies. While recent work demonstrates that l…
cs.LG2024
Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts
Jing-Cheng Pang, Si-Hang Yang, Kaiyuan Li +4
Reinforcement learning (RL) trains agents to accomplish complex tasks through environmental interaction data, but its capacity is also limited by the scope of the available data. T…