activity
20222026
most citedNetwork Topology Optimization via Deep Reinforcement Learning

3 citations · 4 across the 17 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees

Yu Chen, Ruishuo Chen, Xun Wang +2

Loading reusable skill documents into a bounded context window is now the primary way large language model (LLM) agents acquire task-specific capabilities, which makes skill select…

cs.AI2026

When Personal Memory Has No Single Answer: Evaluating LLM Agents under Irreducible Conflict

Lu Yang, Shusheng Xu, Zhuoran Li +2

LLM agents increasingly maintain personal memory across sessions, but it can conflict. Preferences depend on context, behavior evolves, and sources can conflict. When a query lacks…

cs.AI2026

Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies

Zhuoran Li, Hai Zhong, Xun Wang +3

Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination. Crucially, enhancing policy expressiveness is pivotal for achieving supe…

cs.AI2025

Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling

Zhuoran Li, Ruishuo Chen, Hai Zhong +1

Effective multi-user delay-constrained scheduling is crucial in various real-world applications, including embodied AI, instant messaging, live streaming, and data center managemen…

cs.AI2024

Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration

Hai Zhong, Xun Wang, Zhuoran Li +1

Offline-to-Online Reinforcement Learning has emerged as a powerful paradigm, leveraging offline data for initialization and online fine-tuning to enhance both sample efficiency and…

cs.AI2023★ 1 cited

Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RL

Zhuoran Li, Ling Pan, Jiatai Huang +1

We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL). Different from existing algorithms that rely mainly on conserva…