5 papers
Principled Learning-to-Communicate with Quasi-Classical Information Structures
Xiangyu Liu, Haoyi You, Kaiqing Zhang
Learning-to-communicate (LTC) in partially observable environments has received increasing attention in deep multi-agent reinforcement learning, where the control and communication…
Online Learning and Equilibrium Computation with Ranking Feedback
Mingyang Liu, Yongshan Chen, Zhiyuan Fan +3
Online learning in arbitrary, and possibly adversarial, environments has been extensively studied in sequential decision-making, and it is closely connected to equilibrium computat…
Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II
Yi Tian, Kaiqing Zhang, Russ Tedrake +1
We study the problem of state representation learning for control from partial and potentially high-dimensional observations. We approach this problem via cost-driven state represe…
MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning
Chanwoo Park, Seungju Han, Xingzhi Guo +3
Leveraging multiple large language models (LLMs) to build collaborative multi-agentic workflows has demonstrated significant potential. However, most previous studies focus on prom…
Provable Partially Observable Reinforcement Learning with Privileged Information
Yang Cai, Xiangyu Liu, Argyris Oikonomou +1
Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL). In practice, certain \emph{privileged information}, e.g.,…