activity
20192026
most citedLearning Structured Communication for Multi-agent Reinforcement Learning

7 citations · 27 across the 28 of their papers we have counts for

collaborators
Showing cs.AIShow all

5 papers · 1 filter

cs.AI2025

Reinforced Reasoning for Embodied Planning

Di Wu, Jiaxin Fan, Junzhe Zang +4

Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and natural language goals. While recent vision-language models (VLMs)…

cs.AI2025

Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding

Shiyue Wang, Haozheng Xu, Yuhan Zhang +4

Multi-Agent Path Finding (MAPF) is a fundamental problem in artificial intelligence and robotics, requiring the computation of collision-free paths for multiple agents navigating f…

cs.AI2025★ 1 cited

A Survey of Automatic Prompt Engineering: An Optimization Perspective

Wenwu Li, Xiangfeng Wang, Wenhao Li +1

The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than we…

cs.AI2023

Can language agents be alternatives to PPO? A Preliminary Empirical Study On OpenAI Gym

Junjie Sheng, Zixiao Huang, Chuyun Shen +5

The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in t…

cs.AI2023

Negotiated Reasoning: On Provably Addressing Relative Over-Generalization

Junjie Sheng, Wenhao Li, Bo Jin +3

Over-generalization is a thorny issue in cognitive science, where people may become overly cautious due to past experiences. Agents in multi-agent reinforcement learning (MARL) als…