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Responsible Emergent Multi-Agent Behavior
Responsible AI has risen to the forefront of the AI research community.
Policy-Value Alignment and Robustness in Search-based Multi-Agent Learning
Large-scale AI systems that combine search and learning have reached super-human levels of performance in game-playing, but have also been shown to fail in surprising ways.
Collective Obfuscation and Crowdsourcing
Crowdsourcing technologies rely on groups of people to input information that may be critical for decision-making.
Multi-Agent Curricula and Emergent Implicit Signaling
We show that pursuers trained with our strategy exchange more than twice as much information (in bits) than baseline methods, indicating that our method has learned, and relies heavily on, the exchange of implicit signals.
Cooperative Multi-Agent Fairness and Equivariant Policies
We study fairness through the lens of cooperative multi-agent learning.
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems
In this work, we study emergent communication through the lens of cooperative multi-agent behavior in nature.
Multi-agent Reinforcement Learning
reinforcement-learning
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