73 citations · 115 across the 28 of their papers we have counts for
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cs.AI2024
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models
Muhan Lin, Shuyang Shi, Yue Guo +6
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 m…
cs.AI2024★ 1 cited
Multi-Agent Transfer Learning via Temporal Contrastive Learning
Weihao Zeng, Joseph Campbell, Simon Stepputtis +1
This paper introduces a novel transfer learning framework for deep multi-agent reinforcement learning. The approach automatically combines goal-conditioned policies with temporal c…