Papers by Muhan Lin
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models (2024.findings-emnlp)
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Muhan Lin, Shuyang Shi, Yue Guo, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Simon Stepputtis, Joseph Campbell, Katia Sycara
| Challenge: | Reward hacking is a problem in reinforcement learning where the ability to specify the desired behavior of a reward function is difficult. |
| Approach: | They propose to use feedback as a potential-based shaping function to solicit and apply feedback from large language models to improve convergence speed and policy returns. |
| Outcome: | The proposed method improves convergence speed and policy returns over baselines even with significant ranking errors and eliminates the need for complex post-processing of reward functions. |