StarCraft Multi-Agent Challenge (SMAC)

The StarCraft Multi-Agent Challenge (SMAC) is a benchmark that provides simulated environments for evaluating the coordination, negotiation, and cooperation of autonomous artificial agents in multi-agent systems.

Papers using StarCraft Multi-Agent Challenge (SMAC) (10)

Smacv2: An Improved Benchmark For Cooperative Multi-agent Reinforcement Learning2022 · 26 citesSmac-hard: Enabling Mixed Opponent Strategy Script And Self-play On SMAC2024AGENTIC AI IN MULTI-AGENT SYSTEMS: EXPLORING THE COORDINATION, NEGOTIATION, AND COOPERATION OF AUTONOMOUS ARTIFICIAL AGENTS IN COMPETITIVE AND COLLABORATIVE DIGITAL ECOSYSTEMS2026Closed-Loop Vision-Language Planning for Multi-Agent Coordination2025Low-rank Agent-specific Adaptation (lorasa) For Multi-agent Policy Learning2025Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation2024 · 4 citesSemi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients2021 · 1 citesMulti-Task Multi-Agent Shared Layers are Universal Cognition of Multi-Agent Coordination2023Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models2024Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration2024