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Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning

Autor: Qiu, Shuang, Zhang, Dake, Yang, Rui, Lyu, Boxiang, Zhang, Tong

This paper investigates multi-objective reinforcement learning (MORL), which focuses on learning Pareto optimal policies in the presence of multiple reward functions. Despite MORL's significant empirical success, there is still a lack of satisfactory

Externí odkaz: http://arxiv.org/abs/2407.17466

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Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning

Autor: Zhang, Dake, Lyu, Boxiang, Qiu, Shuang, Kolar, Mladen, Zhang, Tong

We study risk-sensitive reinforcement learning (RL), a crucial field due to its ability to enhance decision-making in scenarios where it is essential to manage uncertainty and minimize potential adverse outcomes. Particularly, our work focuses on app

Externí odkaz: http://arxiv.org/abs/2407.07631

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ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading

Autor: Zhang, Dake, Pradeep, Ronak

With the rapid growth and spread of online misinformation, people need tools to help them evaluate the credibility and accuracy of online information. Lateral reading, a strategy that involves cross-referencing information with multiple sources, may

Externí odkaz: http://arxiv.org/abs/2306.07875

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