Jaderberg, M;
Czarnecki, WM;
Dunning, I;
Marris, L;
Lever, G;
Castaneda, AG;
Beattie, C;
... Graepel, T; + view all
(2019)
Human-level performance in 3D multiplayer games with population-based reinforcement learning.
Science
, 364
(6443)
pp. 859-865.
10.1126/science.aau6249.
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Abstract
Reinforcement 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.
| Type: | Article |
|---|---|
| Title: | Human-level performance in 3D multiplayer games with population-based reinforcement learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1126/science.aau6249 |
| Publisher version: | https://doi.org/10.1126/science.aau6249 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, TIME, GO |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10076318 |
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