Abstract: A continuous space pursuit-evasion game algorithm based on multi-group deep reinforcement learning is proposed to solve the problems in continuous space pursuit-evasion game(PEG). In order to avoid the insufficient curse of dimensionality of continuous space in traditional reinforcement learning，a TSK fuzzy inference model is established to represent the continuous space.And a pursuit-evasion game algorithm based on multi-group deep reinforcement learning is designed for the complex and time-consuming problems of discrete action self-learning.The simulation environment and motion model were designed by taking the PEG problem of a four-wheel vehicle as an example, and the simulation experiments were carried out with Q-learning algorithm, reinforcement learning algorithm based on qualification trace and genetic algorithm based on reward, respectively. The simulated results show that the continuous space PEG algorithm can be used to solve the problem of continuous space pursuit-evasion game well，and continuously improve the ability to address problems with the increase in learning times，and has the comparative advantages of less time consuming for independent learning and short application time.

Key words: pursuit-evasiongame, continuousspace, deepQnetwork, neuralnetwork, differentialgame, intelligentvehicle