Method for designing terminal guidance law based on deep reinforcement learning
US12305967B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 30, 2024 |
| Grant date | May 20, 2025 |
| Priority date | — |
| Expiry date | Jan 30, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure discloses a method for designing a terminal guidance law based on deep reinforcement learning, and relates to the field of missile and rocket guidance. The method includes: establishing a relative kinematics equation between a missile and a target in a longitudinal plane of a target interception terminal guidance section of the missile; to adapt to a research paradigm of reinforcement learning, abstracting a research problem and modeling as a Markov decision process; building an algorithm network, and setting algorithm parameters, where a selected deep reinforcement learning algorithm is a deep Q-network (DQN); in a terminal guidance process of each round, obtaining a sufficient number of training samples through Q-learning, training a neural network and updating a target network at fixed frequencies respectively, and continuously repeating the above process before set learning rounds are reached.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.