Intelligent Quadruped Robot Locomotion using PPO Agent

This paper presents the application of the Proximal Policy Optimization (PPO) algorithm in Reinforcement Learning (RL) for autonomous locomotion in quadruped robots. These robots are increasingly used as transformative tools in industrial applications, such as transporting goods and conducting surveillance, especially in challenging or remote environments. The PPO agent interacts with the quadruped environment to learn an optimal control policy that enables the robot to navigate complex and varied terrains with stability and reliability. The agent takes states (observations) as inputs and outputs actions for which a delayed reward is received. The PPO algorithm optimizes policy updates within safe bounds, balancing exploration and exploitation to enhance adaptability and minimize instability in control tasks. This approach helped a four-legged robot walk steadily and efficiently in a simulated environment using a PPO algorithm, showing strong stability and smooth walking.