60 citations · 153 across the 26 of their papers we have counts for
5 papers · 1 filter
Random Policy Enables In-Context Reinforcement Learning within Trust Horizons
Weiqin Chen, Santiago Paternain
Pretrained foundation models have exhibited extraordinary in-context learning performance, allowing zero-shot generalization to new tasks not encountered during pretraining. In the…
Provable Domain Adaptation for Offline Reinforcement Learning with Limited Samples
Weiqin Chen, Xinjie Zhang, Sandipan Mishra +1
Offline reinforcement learning (RL) learns effective policies from a static target dataset. The performance of state-of-the-art offline RL algorithms notwithstanding, it relies on…
Multi-agent assignment via state augmented reinforcement learning
Leopoldo Agorio, Sean Van Alen, Miguel Calvo-Fullana +2
We address the conflicting requirements of a multi-agent assignment problem through constrained reinforcement learning, emphasizing the inadequacy of standard regularization techni…
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms
Weiqin Chen, Mark S. Squillante, Chai Wah Wu +1
We devise a control-theoretic reinforcement learning approach to support direct learning of the optimal policy. We establish various theoretical properties of our approach, such as…
Adaptive Primal-Dual Method for Safe Reinforcement Learning
Weiqin Chen, James Onyejizu, Long Vu +5
Primal-dual methods have a natural application in Safe Reinforcement Learning (SRL), posed as a constrained policy optimization problem. In practice however, applying primal-dual m…