GAIG Game AI Research Group @ QMUL

The Multi-Agent Reinforcement Learning in MalmO (MARLO) Competition

2018
Perez-Liebana, Diego and Hofmann, Katja and Mohanty, Sharada Prasanna and Kuno, Noburu and Kramer, Andre and Devlin, Sam and Gaina, Raluca D and Ionita, Daniel

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 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.
Arxiv: https://arxiv.org/abs/1901.08129 
YouTube: https://youtu.be/B4GbuPWlC5Q 

Cite this work

@inproceedings{perez2018multi,
author= {Perez-Liebana, Diego and Hofmann, Katja and Mohanty, Sharada Prasanna and Kuno, Noburu and Kramer, Andre and Devlin, Sam and Gaina, Raluca D and Ionita, Daniel},
title= {{The Multi-Agent Reinforcement Learning in MalmO (MARLO) Competition}},
year= {2018},
booktitle= {{Challenges in Machine Learning (CiML; NeurIPS Workshop)}},
pages= {1--4},
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 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.},
}

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