The invention relates to an intelligent parking method based on model
reinforcement learning, which uses Monte Carlo tree search, a vehicle
kinematics model, an action classification network and a state value fitting network, and specifically comprises the following steps: S1, combining the Monte Carlo tree search with the action classification network and the vehicle
kinematics model to obtain aparking data pre-training model; S2, training a state value fitting network according to parking data generated by the parking pre-training model; S3, combining the trained state value fitting networkwith a Monte Carlo tree search and action classification network to form an online driving strategy model; and S4, enabling the parking on-line driving strategy model to receive the
parking space andthe vehicle motion information in a rolling
time domain mode, generate a control instruction at each time interval, and send the control instruction to a vehicle
motion control module to control thetarget vehicle to complete parking. Compared with the prior art, the method has the advantages that the final parking course angle and success rate are better, the influence of the accuracy of the vehicle model on the final parking effect is reduced, and the like.