Implementation of DDPG-Based Reinforcement Learning Control for Self-Balancing Motorcycle

This paper presents the implementation of a Deep Deterministic Policy Gradient (DDPG) algorithm in Reinforcement Learning (RL) for self-balancing a motorcycle. The DDPG agent iteratively interacts with the motorcycle environment to develop an optimal control policy, utilizing states such as position and velocity, and actions like motor torque. The study evaluates the performance through simulations and real-time experimentation, demonstrating the algorithm's effectiveness in balancing the motorcycle across various leaning angles and in handling external disturbances and model uncertainties. Comparative analysis with a traditional PD controller highlights DDPG's faster response times, improved disturbance rejection, and enhanced adaptability to uncertainties. The results underscore the potential of RL algorithms in enhancing motorcycle control systems for safer and more efficient operation.