Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality

Kavli Affiliate: Yi Zhou | First 5 Authors: Shaocong Ma, Shaocong Ma, , , | Summary: The goal of robust constrained reinforcement learning (RL) is to optimize an agent’s performance under the worst-case model uncertainty while satisfying safety or resource constraints. In this paper, we demonstrate that strong duality does not generally hold in robust […]


Continue.. Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality

Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality

Kavli Affiliate: Yi Zhou | First 5 Authors: Shaocong Ma, Shaocong Ma, , , | Summary: The goal of robust constrained reinforcement learning (RL) is to optimize an agent’s performance under the worst-case model uncertainty while satisfying safety or resource constraints. In this paper, we demonstrate that strong duality does not generally hold in robust […]


Continue.. Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality

PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs

Kavli Affiliate: Xiang Zhang | First 5 Authors: Zhilin Zhang, Zhilin Zhang, , , | Summary: Multi-agent systems built upon large language models (LLMs) have demonstrated remarkable capabilities in tackling complex compositional tasks. In this work, we apply this paradigm to the paper-to-poster generation problem, a practical yet time-consuming process faced by researchers preparing for […]


Continue.. PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs