Papers › Robust Reinforcement Learning under model misspecification

Robust Reinforcement Learning under model misspecification

29 Mar 2021arXiv:2103.15370archive 2025-07-28

Lebin Yu, Jian Wang, Xudong Zhang

Reinforcement learning has achieved remarkable performance in a wide range of tasks these days. Nevertheless, some unsolved problems limit its applications in real-world control. One of them is model misspecification, a situation where an agent is trained and deployed in environments with different transition dynamics. We propose an novel framework that utilize history trajectory and Partial Observable Markov Decision Process Modeling to deal with this dilemma. Additionally, we put forward an efficient adversarial attack method to assist robust training. Our experiments in four gym domains validate the effectiveness of our framework.

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Adversarial Attack Reinforcement Learning Reinforcement Learning (RL)model reinforcement-learning

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