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This research introduces HORIZON, a hierarchical multi-agent system designed to adapt in real-time to unknown opponents within the Lux AI environment under partial observability. By decoupling short-term control from cross-match meta-reasoning and utilizing belief tracking, the agent demonstrates superior tactical execution and adaptation compared to standard recurrent or feed-forward baselines. The work advances the state of zero-shot opponent modeling through explicit inference of hidden game parameters and dynamic policy mixtures.
Hierarchical Belief Modeling for Zero-Shot Opponent Adaptation in Partially Observable Multi-Agent Navigation
arXiv:2609.12422v1 Announce Type: new Abstract: Lux AI Season 3 requires agents to act under partial observability, randomized episode level dynamics, and a best of five match structure that rewards both tactical execution and fast adaptation. We present HORIZON, a hierarchical agent that combines symmetry aware spatial perception, dual memory belief tracking, relic centric graph attention, information gain driven exploration, and an opponent conditioned policy mixture. HORIZON separates short horizon control from cross match meta reasoning, while auxiliary belief and world model objectives stabilize learning. Trained with PPO in a large scale JAX simulator, the resulting agent explicitly infers hidden game parameters and opponent style. Experiments show consistent gains in match win rate, episode win rate, adaptation gain, and league rating over strong recurrent and feed forward baselines.
Abstract:Lux AI Season 3 requires agents to act under partial observability, randomized episode level dynamics, and a best of five match structure that rewards both tactical execution and fast adaptation. We present HORIZON, a hierarchical agent that combines symmetry aware spatial perception, dual memory belief tracking, relic centric graph attention, information gain driven exploration, and an opponent conditioned policy mixture. HORIZON separates short horizon control from cross match meta reasoning, while auxiliary belief and world model objectives stabilize learning. Trained with PPO in a large scale JAX simulator, the resulting agent explicitly infers hidden game parameters and opponent style. Experiments show consistent gains in match win rate, episode win rate, adaptation gain, and league rating over strong recurrent and feed forward baselines.
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
arXiv:2609.12422 [cs.AI]
(or arXiv:2609.12422v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.12422 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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