WebDespite the recent advances of deep reinforcement learning (DRL), agents trained by DRL tend to be brittle and sensitive to the training environment, especially in the multi-agent scenarios. In the multi-agent setting, a DRL agent's policy can easily get stuck in a poor local optima w.r.t. its training partners - the learned policy may be only locally optimal to other … WebHe received his Ph.D. degree from Tsinghua University in 2004. He was a recipient of the National Science Fund for Distinguished Young Scholars. Currently, he is a senior editor of International Journal of Robotics Research. ... Ha D. Reinforcement learning for improving agent design. Artificial Life, 2024, 25(4): ...
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WebApr 6, 2024 · The overall framework is named "confidence-aware reinforcement learning" (CARL). The condition to switch between the RL policy and the baseline policy is analyzed and presented. Driving in a two ... WebIIIS, Tsinghua University MMW Building S-221 100084, Beijing, China +8610-62773713 Ext. 6221 chongjie at tsinghua.edu.cn. About. ... We also have openings for research interns and post-docs in the areas related to Deep Reinforcement Learning, Multi … diamond on the rocks schedule
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http://ivg.au.tsinghua.edu.cn/people/Liangliang_Ren/ http://ivg.au.tsinghua.edu.cn/DRLCV/ WebAlmost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition Zihan Zhang Department of Automation Tsinghua University [email protected] Yuan Zhou Department of ISE University of Illinois at Urbana-Champaign [email protected] Xiangyang Ji Department of Automation Tsinghua … diamond on soles of her shoes