A Mixed Generative Adversarial Imitation Learning Based Vehicle Path Planning Algorithm

Vehicle path planning is one of the effective ways to relieve the huge traffic flow pressure of modern urban transportation system, and it is also an important way to realize carbon emission reduction and to build green transportation system as well as smart city. At present, the artificial intelligence (AI) algorithms with reinforcement learning (RL) as the mainstream have achieved great success in the field of vehicle path planning. However, RL only conducts policy learning based on the evaluation feedback of the environment, whereas imitation learning (IL) can obtain more direct feedback from expert decision data, and then obtain a decision model close to the expert level by comparing with RL. At present, there are very few vehicle path planning algorithms based on IL, and they are often hindered by the compounding error and sample complexity dilemma, resulting in poor path planning effectiveness. In order to overcome these problems, in this paper, a mixed generative adversarial IL (MixGAIL) algorithm has been proposed, which effectively integrates the transition aware adversarial IL (TAIL) and generative adversarial IL (GAIL) based on minimum-distance functions (MIMIC-MD) methods under the framework of GAIL. In order to overcome the optimization dilemma of non-convex and non-smooth objective function after the integration, the proposed MixGAIL uses mixed policy gradient actor-critic model with random escape term and filter optimization (MPGACEF), and pioneers the noise projected subgradient descent method with momentum (MNPSGD) for global optimization. Experiments have shown that by learning expert decision data, MixGAIL has better vehicle path planning performance and faster iteration speed than classic IL algorithms such as behavioral cloning (BC), dataset aggregation (DAgger), feature expectation matching (FEM), game theoretical appraisal learning (GTAL), TAIL, and MIMIC-MD, and is closer to expert level.