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The Multi-Agent Reinforcement Learning in Malm\"O (MARL\"O) Competition
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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.
Forward citations
Cited by 5 Pith papers
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TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft
TeamCraft presents a large multi-modal, multi-agent Minecraft benchmark and shows that current models generalize poorly to novel goals, scenes, and team sizes.
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AeroDuo: Aerial Duo for UAV-based Vision and Language Navigation
Two drones at different altitudes, one guided by a vision-language model and one by a local navigator, reach targets more often than single-drone baselines on a new UAV navigation benchmark.
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Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review
A survey proposing adaptability as a three-part taxonomy (learning, policy, scenario-driven) for organizing and evaluating MARL under changing conditions.
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Plancraft: an evaluation dataset for planning with LLM agents
A new Minecraft crafting benchmark shows current LLM agents solve only a minority of planning tasks, and vision-language models almost never succeed on image observations.
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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.
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