1 citations · 1 across the 7 of their papers we have counts for
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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…
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…
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…
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…