Name
Title | ||
|---|---|---|
Learning Continuous 3-DoF Air-to-Air Close-in Combat Strategy using Proximal Policy Optimization |
Abstract | ||
|---|---|---|
Air-to-air close-in combat is based on many basic fighter maneuvers and can be largely modeled as an algorithmic function of inputs. This paper studies autonomous close-in combat, to learn new strategy that can adapt to different circumstances to fight against an opponent. Current methods for learning close-in combat strategy are largely limited to discrete action sets whether in the form of rules, actions or sub-polices. In contrast, we consider one-on-one air combat game with continuous action space and present a deep reinforcement learning method based on proximal policy optimization (PPO) that learns close-in combat strategy from observations in an end-to-end manner. The state space is designed to promote the learning efficiency of PPO. We also design a minimax strategy for the game. Simulation results show that the learned PPO agent is able to defeat the minimax opponent with about 97% win rate. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CoG51982.2022.9893690 | 2022 IEEE Conference on Games (CoG) |
Keywords | DocType | ISSN |
air-combat,reinforcement learning,proximal policy optimization,flight simulation | Conference | 2325-4270 |
ISBN | Citations | PageRank |
978-1-6654-5990-7 | 0 | 0.34 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luntong Li | 1 | 0 | 0.34 |
| Zhiming Zhou | 2 | 0 | 0.34 |
| Jiajun Chai | 3 | 0 | 0.68 |
| Zhiyu Liu | 4 | 16 | 10.55 |
| Yuanheng Zhu | 5 | 0 | 0.68 |
| Jian-Qiang Yi | 6 | 695 | 89.71 |