Reinforcement Learning-based Autonomous Parking with Expert Demonstrations

As an important task in autonomous driving of intelligent vehicles, the autonomous parking technology has a broad market potential. The traditional parking algorithm based on trajectory planning often results in significant tracking errors due to the non-linearity of vehicle dynamics. While general reinforcement learning algorithms may require the assistance of domain experts due to the complexity of the parking scene. However, relying on expert demonstrations can also be problematic due to the high collection cost and potential for uneven quality of the demonstrations. To address these problems, we propose an end-to-end reinforcement learning-based parking method using expert demonstrations. Firstly, we add Gaussian noise to the collected expert demonstrations for data augmentation, which expands the diversity of samples and guides the agent to learn the expert policy quickly. Then we introduce the mixed priority sampling strategy, and employ it on the augmented expert demonstrations and experience obtained from exploration in the environment to jointly train the agent. We evaluate our method on the benchmark algorithm, Soft Actor-Critic, on the CARLA autonomous driving simulation platform with vertical and parallel parking environments. The experimental results indicate that the proposed algorithm can greatly improve the convergence rate and achieve a higher parking success rate.