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Learning Robust Policies via Interpretable Hamilton-Jacobi Reachability-Guided Disturbances

Autor: Hu, Hanyang, Zhang, Xilun, Lyu, Xubo, Chen, Mo

Deep Reinforcement Learning (RL) has shown remarkable success in robotics with complex and heterogeneous dynamics. However, its vulnerability to unknown disturbances and adversarial attacks remains a significant challenge. In this paper, we propose a

Externí odkaz: http://arxiv.org/abs/2409.19746

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Task-Oriented Koopman-Based Control with Contrastive Encoder

Autor: Lyu, Xubo, Hu, Hanyang, Siriya, Seth, Pu, Ye, Chen, Mo

We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop. By prior

Externí odkaz: http://arxiv.org/abs/2309.16077

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Multi-Agent Reach-Avoid Games: Two Attackers Versus One Defender and Mixed Integer Programming

Autor: Hu, Hanyang, Bui, Minh, Chen, Mo

We propose a hybrid approach that combines Hamilton-Jacobi (HJ) reachability and mixed-integer optimization for solving a reach-avoid game with multiple attackers and defenders. The reach-avoid game is an important problem with potential applications

Externí odkaz: http://arxiv.org/abs/2309.13155

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