  StarCraft II is one of the most challenging simulated reinforcement learning
environments; it is partially observable, stochastic, multi-agent, and
mastering StarCraft II requires strategic planning over long time horizons with
real-time low-level execution. It also has an active professional competitive
scene. StarCraft II is uniquely suited for advancing offline RL algorithms,
both because of its challenging nature and because Blizzard has released a
massive dataset of millions of StarCraft II games played by human players. This
paper leverages that and establishes a benchmark, called AlphaStar Unplugged,
introducing unprecedented challenges for offline reinforcement learning. We
define a dataset (a subset of Blizzard's release), tools standardizing an API
for machine learning methods, and an evaluation protocol. We also present
baseline agents, including behavior cloning, offline variants of actor-critic
and MuZero. We improve the state of the art of agents using only offline data,
and we achieve 90% win rate against previously published AlphaStar behavior
cloning agent.
