13 papers
Safe In-Context Reinforcement Learning
Amir Moeini, Minjae Kwon, Alper Kamil Bozkurt +4
In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks without any parameter updates, in…
Beyond Linear Attention: Softmax Transformers Implement In-Context Reinforcement Learning
Zixuan Xie, Xinyu Liu, Claire Chen +3
In-context reinforcement learning (ICRL) studies agents that, after pretraining, adapt to new tasks by conditioning on additional context without parameter updates. Existing theore…
GameChat: Multi-LLM Dialogue for Safe, Agile, and Socially Optimal Multi-Agent Navigation in Constrained Environments
Vagul Mahadevan, Shangtong Zhang, Rohan Chandra
Safe, agile, and socially compliant multi-robot navigation in cluttered and constrained environments remains a critical challenge. This is especially difficult with self-interested…
Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought
Zixuan Xie, Xinyu Liu, Rohan Chandra +1
In-context reinforcement learning (ICRL) refers to the ability of RL agents to adapt to new tasks at inference time without parameter updates by conditioning on additional context.…
Reward Is Enough: LLMs Are In-Context Reinforcement Learners
Kefan Song, Amir Moeini, Peng Wang +4
Reinforcement learning (RL) is a framework for solving sequential decision-making problems. In this work, we demonstrate that, surprisingly, RL emerges during the inference time of…
FACA: Fair and Agile Multi-Robot Collision Avoidance in Constrained Environments with Dynamic Priorities
Jaskirat Singh, Rohan Chandra
Multi-robot systems are increasingly being used for critical applications such as rescuing injured people, delivering food and medicines, and monitoring key areas. These applicatio…