2 citations · 2 across the 15 of their papers we have counts for
15 papers
Entropic Risk-Sensitive Evolutionary Learning and Equilibrium Selection in Coordination Games
Solaleh Mohammadi, Xiang Gao, Kaiqing Zhang
We study risk-sensitive evolutionary learning dynamics and their long-run equilibrium selection behaviors in coordination games. Agents' risk attitudes enter through the classical…
Joint Communication-Control Strategy Optimization with Partially Nested Information Structures: The Linear-Quadratic Case
Haoyi You, Kaiqing Zhang
In this paper, we formalize a joint communication-control strategy optimization (JCCO) problem in multi-agent linear systems with quadratic costs, under the common-information-base…
Regret Minimization with Adaptive Opponents in Repeated Games
Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu +1
In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play. The standard metric of \emph{external regre…
Can LLM Agents Simulate Dynamic Networks? A Case Study on Email Networks with Phishing Synthesis
Siqi Miao, Ziyang Chen, Yuhong Luo +4
While Large Language Model (LLM) multi-agent systems (MAS) offer a transformative approach to simulating human behavior in complex systems, it remains largely unexplored whether th…
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…
Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach
Chanwoo Park, Ziyang Chen, Asuman Ozdaglar +1
Large language models (LLMs) are increasingly deployed as "agents" for decision-making (DM) in interactive and dynamic environments. Yet, since they were not originally designed fo…
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.,…
Decentralized Best-Response-Based Learning in Two-Player Zero-Sum Stochastic Games: A Finite-Sample Analysis
Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar +2
We present a finite-sample analysis of decentralized learning in two-player zero-sum matrix games and stochastic games, with a focus on best-response-based learning algorithms. In…
RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation
Chanwoo Park, Mingyang Liu, Dingwen Kong +2
Reinforcement learning from human feedback (RLHF) has been an effective technique for aligning AI systems with human values, with remarkable successes in fine-tuning large-language…
Do LLM Agents Have Regret? A Case Study in Online Learning and Games
Chanwoo Park, Xiangyu Liu, Asuman Ozdaglar +1
Large language models (LLMs) have been increasingly employed for (interactive) decision-making, via the development of LLM-based autonomous agents. Despite their emerging successes…
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games
Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar +2
We consider two-player zero-sum stochastic games and propose a two-timescale -learning algorithm with function approximation that is payoff-based, convergent, rational, and symm…
Robot Fleet Learning via Policy Merging
Lirui Wang, Kaiqing Zhang, Allan Zhou +2
Fleets of robots ingest massive amounts of heterogeneous streaming data silos generated by interacting with their environments, far more than what can be stored or transmitted with…