RT Journal Article SR Electronic T1 Human-level performance in 3D multiplayer games with population-based reinforcement learning JF Science JO Science FD American Association for the Advancement of Science SP 859 OP 865 DO 10.1126/science.aau6249 VO 364 IS 6443 A1 Jaderberg, Max A1 Czarnecki, Wojciech M. A1 Dunning, Iain A1 Marris, Luke A1 Lever, Guy A1 CastaƱeda, Antonio Garcia A1 Beattie, Charles A1 Rabinowitz, Neil C. A1 Morcos, Ari S. A1 Ruderman, Avraham A1 Sonnerat, Nicolas A1 Green, Tim A1 Deason, Louise A1 Leibo, Joel Z. A1 Silver, David A1 Hassabis, Demis A1 Kavukcuoglu, Koray A1 Graepel, Thore YR 2019 UL http://science.sciencemag.org/content/364/6443/859.abstract AB Artificially intelligent agents are getting better and better at two-player games, but most real-world endeavors require teamwork. Jaderberg et al. designed a computer program that excels at playing the video game Quake III Arena in Capture the Flag mode, where two multiplayer teams compete in capturing the flags of the opposing team. The agents were trained by playing thousands of games, gradually learning successful strategies not unlike those favored by their human counterparts. Computer agents competed successfully against humans even when their reaction times were slowed to match those of humans.Science, this issue p. 859Reinforcement learning (RL) has shown great success in increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete with other agents. We used a tournament-style evaluation to demonstrate that an agent can achieve human-level performance in a three-dimensional multiplayer first-person video game, Quake III Arena in Capture the Flag mode, using only pixels and game points scored as input. We used a two-tier optimization process in which a population of independent RL agents are trained concurrently from thousands of parallel matches on randomly generated environments. Each agent learns its own internal reward signal and rich representation of the world. These results indicate the great potential of multiagent reinforcement learning for artificial intelligence research.