The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition

• URL: http://arxiv.org/abs/1901.08129v2
• Date: Fri, 11 Apr 2025 11:14:27 GMT
• Title: The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
• Authors: Diego Perez-Liebana, Katja Hofmann, Sharada Prasanna Mohanty, Noboru Kuno, Andre Kramer, Sam Devlin, Raluca D. Gaina, Daniel Ionita,
• Abstract summary: The Multi-Agent Reinforcement Learning in Malm"O (MARL"O) competition is a new challenge that proposes research in this domain using multiple 3D games.<n>The goal of this contest is to foster research in general agents that can learn across different games and opponent types.
• Score: 14.726566410348985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Malm\"O (MARL\"O) 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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