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

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.

2 pages plus references

References in corpus (2)

• Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver
• Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

Cited by in corpus (8)

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• Towards robust and domain agnostic reinforcement learning competitions
• Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction