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The Cartographer
The Multi-Agent Reinforcement Learning in MalmΓ (MARLΓ) Competition
January 23, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Diego Perez-Liebana, Katja Hofmann, Sharada Prasanna Mohanty, Noboru Kuno, Andre Kramer, Sam Devlin, Raluca D. Gaina, Daniel Ionita
arXiv ID
1901.08129
Category
cs.AI: Artificial Intelligence
Citations
37
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in MalmΓ (MARLΓ) competition is a new challenge that proposes research in this domain using multiple 3D games. The goal of this contest is to foster research in general agents that can learn across different games and opponent types, proposing a challenge as a milestone in the direction of Artificial General Intelligence.
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