On Developing a UAV Pursuit-Evasion Policy Using Reinforcement Learning
| Autor: | Eric Squires, Laura Strickland, Charles Pippin, Bogdan Vlahov |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Machine learning computer.software_genre Domain (software engineering) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Artificial intelligence Pursuit-evasion business computer |
| Zdroj: | ICMLA |
| DOI: | 10.1109/icmla.2018.00138 |
| Popis: | We present an approach for learning a reactive maneuver policy for a UAV involved in a close-quarters one-on-one aerial engagement. Specifically, UAVs with behaviors learned through reinforcement learning can match or improve upon simple, but effective behaviors for intercept. In this paper, a framework for developing reactive policies that can learn to exploit behaviors is discussed. In particular, the A3C algorithm with a deep neural network is applied to the aerial combat domain. The efficacy of the learned policy is demonstrated in Monte Carlo experiments. An architecture that can transfer the learned policy from simulation to an actual aircraft and its effectiveness in live-flight are also demonstrated. |
| Databáze: | OpenAIRE |
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