The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
This work proposes a new benchmark for the multi-agent reinforcement learning community, though it is incremental as it builds on existing competition frameworks.
The paper introduces the MARLÖ competition to address scalability issues in multi-agent reinforcement learning across diverse 3D games and opponent types, aiming to foster research toward general agents as a step toward Artificial General Intelligence.
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 MalmÖ (MARLÖ) 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.