Quadruped Robot Locomotion Control Using Reinforcement Learning

Controlling quadrupedal robots presents significant challenges due to the complexity of their locomotion. Traditional robot control methods often struggle to control the various movements of these robots. In this study, we apply Deep Reinforcement Learning (DRL) techniques, specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, to design a flexible and autonomous controller for a quadrupedal robot. This Project uses actor-critic models, with the actor issuing control commands and the critic evaluating their effectiveness. We develop a reward system that encourages stability, energy efficiency, and obstacle avoidance. Our experiments demonstrate the effectiveness of DDPG in addressing quadrupedal robot control challenges, underscoring the potential of DRL methods in enhancing robotic systems for real-world applications.