Anton Gurevich
Papers
1
Total Citations
5
H-Index
1
About
Anton Gurevich is a researcher focused on advancing the frontiers of multi-agent systems and data-efficient machine learning. His work addresses the critical challenge of enabling autonomous agents to learn effective behaviors from limited data, particularly within homogeneous multi-agent environments. Gurevich’s most cited paper, "Learning a data-efficient model for a single agent in homogeneous multi-agent systems" (2023), introduces a novel framework that allows a single agent to model and predict the collective dynamics of its peers, drastically reducing the need for extensive training data. This contribution is foundational for scalable swarm robotics and distributed AI, where communication and computational resources are constrained. With 5 citations in its first year, the paper signals growing recognition of its practical impact. Gurevich’s research bridges theoretical modeling and real-world deployment, offering a pathway to more adaptive, resource-aware autonomous systems. His work is particularly notable for its emphasis on interpretability and efficiency, making it accessible to both academic researchers and industry practitioners working on collaborative robotics, traffic management, and decentralized control.
Research Focus
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Top Papers
- 1