5 citations · 8 across the 24 of their papers we have counts for
12 papers · 1 filter
Vector Symbolic Policy Gradient
Ryozo Masukawa, Sanggeon Yun, SungHeon Jeong +6
We answer this question with Vector-Symbolic Policy Gradient (VSPG), a discrete-action actor that represents each action by a unit-norm hypervector and scores it by similarity to t…
Minimal Markovization via Stable Quotients in Holonomy-Cover Decision Processes
Zuyuan Zhang, Yongshan Chen, Mahdi Imani +1
An agent acting under partial observability must retain a recursively updateable statistic of history that restores the Markov property, but the smallest such statistic is generall…
FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
Yuchen Hou, Yongshan Chen, Zhuowen Zou +4
Federated reinforcement learning enables decentralized agents to collaboratively improve policies or value estimates without exchanging raw trajectories. However, FedAvg-style para…
Metric-Gradient Projection for Stable Multi-Agent Policy Learning
Zuyuan Zhang, Sizhe Tang, Mahdi Imani +1
General-sum multi-agent learning is often governed by a stacked update field in which each agent's policy update changes the optimization landscape faced by the others. This coupli…
Interactive Critique-Revision Training for Reliable Structured LLM Generation
Fei Xu Yu, Zuyuan Zhang, Mahdi Imani +2
In structured decision-making workflows such as form filling, compliance checking, and maintenance reporting, LLM outputs must be locally correct, globally consistent, and auditabl…
NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
Sizhe Tang, Zuyuan Zhang, Mahdi Imani +1
Monte Carlo Tree Search (MCTS) scales poorly in cooperative multi-agent domains because expansion must consider an exponentially large set of joint actions, severely limiting explo…
-Musketeers: Reinforcement Learning Shapes Collaboration Among Language Models
Ryozo Masukawa, Sanggeon Yun, Hyunwoo Oh +8
Recent progress in reinforcement learning with verifiable rewards (RLVR) shows that small, specialized language models (SLMs) can exhibit structured reasoning without relying on la…
Manifold-Constrained Energy-Based Transition Models for Offline Reinforcement Learning
Zeyu Fang, Zuyuan Zhang, Mahdi Imani +1
Model-based offline reinforcement learning is brittle under distribution shift: policy improvement drives rollouts into state--action regions weakly supported by the dataset, where…
Geometry of Drifting MDPs with Path-Integral Stability Certificates
Zuyuan Zhang, Mahdi Imani, Tian Lan
Real-world reinforcement learning is often \emph{nonstationary}: rewards and dynamics drift, accelerate, oscillate, and trigger abrupt switches in the optimal action. Existing theo…
ACDZero: MCTS Agent for Mastering Automated Cyber Defense
Yu Li, Sizhe Tang, Rongqian Chen +5
Automated cyber defense (ACD) seeks to protect computer networks with minimal or no human intervention, reacting to intrusions by taking corrective actions such as isolating hosts,…
Global Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representations
Shu Hong, Yongsheng Mei, Mahdi Imani +1
Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging due to the discre…
Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data
Yongsheng Mei, Mahdi Imani, Tian Lan
Bayesian optimization (BO) has established itself as a leading strategy for efficiently optimizing expensive-to-evaluate functions. Existing BO methods mostly rely on Gaussian proc…