AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning

Exploration is another key challenge in complex environments such as StarCraft. There are up to 1026 possible actions available to one of our agents at each time step, and the agent must make thousands of actions before learning if it has won or lost the game. Finding winning strategies is challenging in such a massive solution space. Even with a strong self-play system and a diverse league of main and exploiter agents, there would be almost no chance of a system developing successful strategies in such a complex environment without some prior knowledge. Learning human strategies, and ensuring that the agents keep exploring those strategies throughout self-play, was key to unlocking AlphaStar's performance.