1 citations · 1 across the 14 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…
Active Offline-to-Online Reinforcement Learning
Alper Kamil Bozkurt, Shangtong Zhang, Yuichi Motai
Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online…
Convergence of Two-Timescale Markovian Stochastic Approximations with Applications in Reinforcement Learning
Vagul Mahadevan, Claire Chen, Shuze Daniel Liu +1
This work studies the convergence of two-timescale stochastic approximations (SA), a class of iterative algorithms that update two sets of parameters in fast and slow timescales re…
Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning
Minjae Kwon, Amir Moeini, Shangtong Zhang +1
Safe in-context reinforcement learning (ICRL) adapts online from interaction history without test-time parameter updates while controlling episode cost under a safety budget. Under…
Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning
Alper Kamil Bozkurt, Xiaoan Xu, Shangtong Zhang +2
In offline-to-online reinforcement learning (O2O-RL), policies are first safely trained offline using previously collected datasets and then further fine-tuned for tasks via limite…
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…
Counterfactual Explanations for Continuous Action Reinforcement Learning
Shuyang Dong, Shangtong Zhang, Lu Feng
Reinforcement Learning (RL) has shown great promise in domains like healthcare and robotics but often struggles with adoption due to its lack of interpretability. Counterfactual ex…
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…