StarCraft Multi-Agent Challenge
Emerging9papers using it
2022first seen
The StarCraft Multi-Agent Challenge is a benchmark that evaluates multi-agent coordination and decision-making in cooperative environments, featuring decentralized control, partial observability, and long-horizon decision-making.
Papers using StarCraft Multi-Agent Challenge (9)
- SMAC-Talk: A Natural Language Extension of the StarCraft Multi-Agent Challenge for Large Language ModelsCCKS: Consensus-based Communication and Knowledge SharingAutonomous Partner Selection for Cooperative Multi-Agent Reinforcement LearningAGENTIC AI IN MULTI-AGENT SYSTEMS: EXPLORING THE COORDINATION, NEGOTIATION, AND COOPERATION OF AUTONOMOUS ARTIFICIAL AGENTS IN COMPETITIVE AND COLLABORATIVE DIGITAL ECOSYSTEMSMulti-Agent Deep Reinforcement Learning Under Constrained CommunicationsTransformer World Model for Sample Efficient Multi-Agent Reinforcement LearningHybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement LearningRethinking Individual Global Max in Cooperative Multi-Agent
Reinforcement LearningMAC-PO: Multi-Agent Experience Replay via Collective Priority
Optimization