Abstract
We present a different view for AlphaStar (AS), the program achieving Grand-Master level in the game StarCraft II. It is considered big progress for AI research. However, in this paper, we present problems with the AS, some of which are the defects of it, and some of which are important details that are neglected in its article. These problems arise two questions. One is that what can we get from the built of AS? The other is that does the battle between it with humans fair? After the discussion, we present the future research directions for these problems. Our study is based on a reproduction code of the AS, and the codes are available online.
Related Papers
Transformer World Model for Sample Efficient Multi-Agent Reinforcement Learning2025-06-23A Benchmark for Generalizing Across Diverse Team Strategies in Competitive Pokémon2025-06-12NeuroPAL: Punctuated Anytime Learning with Neuroevolution for Macromanagement in Starcraft: Brood War2025-06-12Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation2025-06-01Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning2025-05-19AVA: Attentive VLM Agent for Mastering StarCraft II2025-03-07Trajectory-Class-Aware Multi-Agent Reinforcement Learning2025-03-03SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning2025-03-03