Learning Multi-Agent Coordination via Sheaf-ADMM

Decentralized Multi-Agent Reinforcement Learning for Autonomous Aircraft Traffic Management in AAM Corridor Networks

This research paper addresses the challenge of managing high-density autonomous aircraft traffic in Advanced Air Mobility (AAM) corridors. T

Skill-MAS: A Meta-Skill Approach to Improving Multi-Agent Systems Without Retraining

Skill-MAS proposes a novel approach to LLM-based automatic Multi-Agent Systems (MAS) generation that bridges the gap between inference-time

Skill-MAS: A Meta-Skill Approach to Improving Multi-Agent Systems Without Retraining

Skill-MAS proposes a novel approach to LLM-based automatic Multi-Agent Systems (MAS) generation that bridges the gap between inference-time

AgentGym-RL: A Reinforcement Learning Framework for Training LLM Agents in Multi-Turn Decision Making

This paper introduces AgentGym-RL, a unified reinforcement learning framework for training LLM agents to perform multi-turn interactive deci

AgentGym-RL: A Reinforcement Learning Framework for Training LLM Agents in Multi-Turn Decision Making

This paper introduces AgentGym-RL, a unified reinforcement learning framework for training LLM agents to perform multi-turn interactive deci

Building agents

ACM Transactions on Evolutionary Learning and Optimization (telo) Vol 6, Issue 2

Multi-Agent Reinforcement Learning Reduces Drone Racing Collisions by 50% While Achieving Champion-Level Performance

This article presents research demonstrating that multi-agent reinforcement learning (MARL) enables superhuman performance in shared, dynami