Muhan Lin — accepted AI-conference papers

2025

Speedup Techniques for Switchable Temporal Plan Graph Optimization

AAAI 2025technical

Multi-Agent Path Finding (MAPF) focuses on planning collision-free paths for multiple agents. However, during the execution of a MAPF plan, agents may encounter unexpected delays, which can lead to inefficiencies, deadlocks, or even collisions. To address these issues, the Switchable Temporal Plan G…

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2024

Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models

Muhan Lin, Shuyang Shi, Yue Guo, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari +3

EMNLP 2024finding

The correct specification of reward models is a well-known challenge in reinforcement learning.Hand-crafted reward functions often lead to inefficient or suboptimal policies and may not be aligned with user values.Reinforcement learning from human feedback is a successful technique that can mitigate…

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