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Almost Optimal Model-Free Reinforcement Learning via ... - NeurIPS

WebMildly Conservative Q-Learning for Offline Reinforcement Learning Jiafei Lyu1∗, Xiaoteng Ma 2∗, Xiu Li1†, Zongqing Lu 3† 1Tsinghua Shenzhen International Graduate School, … 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 … state farm douglas cheung

Jun Zhu

WebMENT LEARNING: SOLVING EXTENSIVE GAMES WITH IMPERFECT INFORMATION Yichi Zhou, Jialian Li, Jun Zhu Dept. of Comp. Sci. & Tech., BNRist Center, Institute for AI, Tsinghua University; RealAI [email protected],[email protected],[email protected] ABSTRACT Posterior … WebOffline Reinforcement Learning with Reverse Model-based Imagination. Advances in Neural Information Processing Systems (NeurIPS), 2024. Lulu Zheng*, Jiarui Chen*, Jianhao … state farm doug scheppmann boulder city