Nanjing University of Science and Technology

Wenjun Yi

Tianhong Xiong

Shu Yang

Jian Huang

Lijun Ma

Guilin Jiang

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.

Growing community of inventors

Nanjing, China

Wenjun Yi