A survey of multi-agent reinforcement learning in video games, plus a proposed five-dimension, MDP-based classification for comparing game complexity.
The Multi-Agent Reinforcement Learning in Malm\"O (MARL\"O) Competition
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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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cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games
A survey of multi-agent reinforcement learning in video games, plus a proposed five-dimension, MDP-based classification for comparing game complexity.