Driving Torque Distribution Strategy of Skid-Steering Vehicles with Knowledge-Assisted Reinforcement Learning

Abstract

Due to the advantages of their drive configuration form, skid-steering vehicles with independent wheel drive systems are widely used in various special applications. However, obtaining a reasonable distribution of the driving torques for the coordinated control of independent driving wheels is a challenging problem. In this paper, we propose a torque distribution strategy based on the Knowledge-Assisted Deep Deterministic Policy Gradient (KA-DDPG) algorithm, in order to minimize the desired value tracking error as well as achieve the longitudinal speed and yaw rate tracking control of skid-steering vehicles. The KA-DDPG algorithm combines knowledge-assisted learning methods with the DDPG algorithm, within the framework of knowledge-assisted reinforcement learning. To accelerate the learning process of KA-DDPG, two assisted learning methods are proposed: a criteria action method and a guiding reward method. The simulation results obtained, considering different scenarios, demonstrate that the KA-DDPG-based torque distribution strategy allows a skid-steering vehicle to achieve high performance, in tracking the desired value. In addition, further simulation results, also, demonstrate the contributions of knowledge-assisted learning methods to the training process of KA-DDPG: the criteria action method speeds up the learning speed by reducing the agent’s random action selection, while the guiding reward method achieves the same result by sharpening the reward function.