Cory Neville
Papers
1
Total Citations
3
H-Index
1
About
Cory Neville is a researcher at the intersection of swarm robotics and reinforcement learning, with a focus on decentralized multi-agent systems. His most-cited work, "Reinforcement Learning Adversarial Swarm Dynamics" (2020), explores how reinforcement learning can optimize control laws for robotic swarms operating in adversarial environments. This paper lays groundwork for enabling autonomous, decentralized coordination among multiple agents—a critical challenge in fields like defense, search-and-rescue, and environmental monitoring. While his citation count is still emerging, Neville’s contributions are notable for bridging theoretical reinforcement learning with practical swarm dynamics, offering a framework for adaptive, resilient multi-robot systems. His work is particularly relevant for researchers interested in scalable AI-driven robotics and adversarial scenarios. As the field grows, Neville’s early insights into adversarial swarm behavior position him as a promising voice in the development of intelligent, cooperative robotic teams.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement Learning Adversarial Swarm Dynamics3 citations · 2020