Outside of normal form (NF) games, this problem setting Two-player, constant-sum games are well studied arises in multi-agent training when dealing with empirical in the literature, but there has been limited games (also called meta-games), where a game payoff progress outside of this setting. We propose Joint tensor is populated with expected outcomes between Policy-Space Response Oracles (JPSRO), an algorithm agents playing an extensive form (EF) game, for example for training agents in n-player, general-sum the StarCraft League (Vinyals et al., 2019) and Policy-Space extensive form games, which provably converges Response Oracles (PSRO) (Lanctot et al., 2017), a recent to an equilibrium. We further suggest correlated variant of which reached state-of-the-art results in Stratego equilibria (CE) as promising meta-solvers, and Barrage (McAleer et al., 2020).

DeepMind, an Alphabet subsidiary, announced Tuesday its efforts to create an artificial intelligence (AI) that functions with human-like performance. Using Quake III Arena's Capture the Flag (CTF), a 3D first-person multiplayer video game, DeepMind taught AI how to work with and against humans to win the game. The rules of CTF haven't changed much since gym class. Two groups of individuals band together to steal the opponent's flag from their territory while also protecting their own. Teams can tag opponents that enter their territory, sending them back to their respective home base.