73 citations · 91 across the 28 of their papers we have counts for
4 papers · 1 filter
Adaptively Coordinating with Novel Partners via Learned Latent Strategies
Benjamin Li, Shuyang Shi, Lucia Romero +7
Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real tim…
Modeling Latent Partner Strategies for Adaptive Zero-Shot Human-Agent Collaboration
Benjamin Li, Shuyang Shi, Lucia Romero +7
In collaborative tasks, being able to adapt to your teammates is a necessary requirement for success. When teammates are heterogeneous, such as in human-agent teams, agents need to…
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…
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…