9 citations · 23 across the 11 of their papers we have counts for
11 papers
Teaching Language Models to Critique via Reinforcement Learning
Zhihui Xie, Jie Chen, Liyu Chen +3
Teaching large language models (LLMs) to critique and refine their outputs is crucial for building systems that can iteratively improve, yet it is fundamentally limited by the abil…
Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games
Weichao Mao, Haoran Qiu, Chen Wang +3
No-regret learning has a long history of being closely connected to game theory. Recent works have devised uncoupled no-regret learning dynamics that, when adopted by all the playe…
Decision Transformer as a Foundation Model for Partially Observable Continuous Control
Xiangyuan Zhang, Weichao Mao, Haoran Qiu +1
Closed-loop control of nonlinear dynamical systems with partial-state observability demands expert knowledge of a diverse, less standardized set of theoretical tools. Moreover, it…
Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction
Haoran Qiu, Weichao Mao, Archit Patke +7
Large language models (LLMs) have been driving a new wave of interactive AI applications across numerous domains. However, efficiently serving LLM inference requests is challenging…
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
Xiangyuan Zhang, Weichao Mao, Saviz Mowlavi +2
We introduce controlgym, a library of thirty-six industrial control settings, and ten infinite-dimensional partial differential equation (PDE)-based control problems. Integrated wi…
Action Dynamics Task Graphs for Learning Plannable Representations of Procedural Tasks
Weichao Mao, Ruta Desai, Michael Louis Iuzzolino +1
Given video demonstrations and paired narrations of an at-home procedural task such as changing a tire, we present an approach to extract the underlying task structure -- relevant…
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Weichao Mao, Lin F. Yang, Kaiqing Zhang +1
Multi-agent reinforcement learning (MARL) algorithms often suffer from an exponential sample complexity dependence on the number of agents, a phenomenon known as \emph{the curse of…
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games
Weichao Mao, Tamer Başar
This paper addresses the problem of learning an equilibrium efficiently in general-sum Markov games through decentralized multi-agent reinforcement learning. Given the fundamental…
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control
Weichao Mao, Kaiqing Zhang, Ruihao Zhu +2
We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes. Both the reward functions and the state transition functions are allowed to vary arb…
POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis
Weichao Mao, Kaiqing Zhang, Qiaomin Xie +1
Monte-Carlo planning, as exemplified by Monte-Carlo Tree Search (MCTS), has demonstrated remarkable performance in applications with finite spaces. In this paper, we consider Monte…
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning
Weichao Mao, Kaiqing Zhang, Erik Miehling +1
Multi-agent reinforcement learning (MARL) under partial observability has long been considered challenging, primarily due to the requirement for each agent to maintain a belief ove…