7 papers
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…
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…