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cs.LG2024★ 1 cited
Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement
Guanwen Xie, Jingzehua Xu, Yiyuan Yang +2
Achieving the effective design and improvement of reward functions in reinforcement learning (RL) tasks with complex custom environments and multiple requirements presents consider…
cs.LG2024
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted Regression
Ziqi Zhang, Zifeng Zhuang, Jingzehua Xu +4
We propose a novel one-step supervised imitation learning (IL) framework called Adversarial Density Regression (ADR). This IL framework aims to correct the policy learned on unknow…