DeepMind’s AlphaStar Final beats 99.8% of human StarCraft 2 players

Alphabet subsidiary DeepMind — which famously developed AlphaZero, a machine learning system that bested world champions in chess, shogi, and Go — returned to the video game domain once again in January with AlphaStar, which tackled Activision Blizzard’s popular real-time strategy title StarCraft 2. It beat top player Grzegorz “MaNa” Komincz and teammate Dario “TLO” Wünsch in a series of 10 matches, but a paper today published in the journal Nature describes a more impressive feat: Further training boosted AlphaStar’s ranking above 99.8% of all active players and earned it the level of GrandMaster — a spot among the top 200 regional players — for all three StarCraft 2 player races (Protoss, Terran, and Zerg).

DeepMind says this latest iteration of AlphaStar — AlphaStar Final — can play a full StarCraft 2 match under “professionally approved” conditions, importantly with limits on the frequency of its actions and by viewing the world through a game camera. It plays on the official StarCraft 2 Battle.net server using the same maps and conditions as human players, and it’s able to continuously self-improve without human intervention, courtesy a combination of general-purpose machine learning techniques including self-play via reinforcement learning, multi-agent learning, and imitation learning.