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A hierarchical reinforcement learning method for missile evasion and guidance.
| Content Provider | Europe PMC |
|---|---|
| Author | Yan, Mengda Yang, Rennong Zhang, Ying Yue, Longfei Hu, Dongyuan |
| Abstract | This paper proposes an algorithm for missile manoeuvring based on a hierarchical proximal policy optimization (PPO) reinforcement learning algorithm, which enables a missile to guide to a target and evade an interceptor at the same time. Based on the idea of task hierarchy, the agent has a two-layer structure, in which low-level agents control basic actions and are controlled by a high-level agent. The low level has two agents called a guidance agent and an evasion agent, which are trained in simple scenarios and embedded in the high-level agent. The high level has a policy selector agent, which chooses one of the low-level agents to activate at each decision moment. The reward functions for each agent are different, considering the guidance accuracy, flight time, and energy consumption metrics, as well as a field-of-view constraint. Simulation shows that the PPO algorithm without a hierarchical structure cannot complete the task, while the hierarchical PPO algorithm has a 100% success rate on a test dataset. The agent shows good adaptability and strong robustness to the second-order lag of autopilot and measurement noises. Compared with a traditional guidance law, the reinforcement learning guidance law has satisfactory guidance accuracy and significant advantages in average time and average energy consumption. |
| Journal | Scientific Reports [Sci Rep] |
| Volume Number | 12 |
| DOI | 10.1038/s41598-022-21756-6 |
| PubMed Central reference number | PMC9640633 |
| Issue Number | 1 |
| PubMed reference number | 36344598 |
| e-ISSN | 20452322 |
| Language | English |
| Publisher | Nature Publishing Group |
| Publisher Date | 2022-11-07 |
| Publisher Place | London |
| Access Restriction | Open |
| Subject Keyword | Aerospace engineering Mechanical engineering |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
