5 papers
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…
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…
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…
The Lagrangian Method for Solving Constrained Markov Games
Soham Das, Santiago Paternain, Luiz F. O. Chamon +1
We propose the concept of a Lagrangian game to solve constrained Markov games. Such games model scenarios where agents face cost constraints in addition to their individual rewards…
Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning
Leopoldo Agorio, Sean Van Alen, Santiago Paternain +2
Constrained multi-agent reinforcement learning offers the framework to design scalable and almost surely feasible solutions for teams of agents operating in dynamic environments to…