1 citations · 1 across the 12 of their papers we have counts for
15 papers
Downstream-Aware Context Selection for Online In-Context Reinforcement Learning
Ruihan A. Li, Shangtong Zhang, Rohan Chandra
In-context reinforcement learning (ICRL) enables large language model agents to adapt to new environments using their interaction history without updating model parameters. However…
Robust Data-Collection Policy Learning for Low-Variance Online Policy Evaluation
Claire Chen, Shuze Daniel Liu, Licheng Luo +3
In reinforcement learning policy evaluation, classic on-policy methods often suffer from high variance when estimating policy performance. To mitigate this issue, behavior policy s…
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…
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.…
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…
Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RL
Aleesha Khurram, Amir Moeini, Shangtong Zhang +1
Despite significant progress and advances in autonomous driving, many end-to-end systems still struggle with domain adaptation (DA), such as transferring a policy trained under cle…
Towards Provable Emergence of In-Context Reinforcement Learning
Jiuqi Wang, Rohan Chandra, Shangtong Zhang
Typically, a modern reinforcement learning (RL) agent solves a task by updating its neural network parameters to adapt its policy to the task. Recently, it has been observed that s…
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…
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…
Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
Zixuan Xie, Xinyu Liu, Rohan Chandra +1
Linear TD() is one of the most fundamental reinforcement learning algorithms for policy evaluation. Previously, convergence rates are typically established under the assumption…
Experience Replay Addresses Loss of Plasticity in Continual Learning
Jiuqi Wang, Rohan Chandra, Shangtong Zhang
Loss of plasticity is one of the main challenges in continual learning with deep neural networks, where neural networks trained via backpropagation gradually lose their ability to…
Group Fairness in Multi-Task Reinforcement Learning
Kefan Song, Runnan Jiang, Rohan Chandra +1
This paper addresses a critical societal consideration in the application of Reinforcement Learning (RL): ensuring equitable outcomes across different demographic groups in multi-t…
Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models
Kefan Song, Jin Yao, Runnan Jiang +2
As Large Language Models (LLMs) become increasingly powerful and accessible to human users, ensuring fairness across diverse demographic groups, i.e., group fairness, is a critical…
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…
A Survey of In-Context Reinforcement Learning
Amir Moeini, Jiuqi Wang, Jacob Beck +4
Reinforcement learning (RL) agents typically optimize their policies by performing expensive backward passes to update their network parameters. However, some agents can solve new…