Learning to Run Faster in a Humanoid Robot Soccer Environment Through Reinforcement Learning

Abstract

Reinforcement learning techniques bring a new perspective to enduring problems. Developing skills from scratch is not only appealing due to the artificial creation of knowledge. It can also replace years of work and refinement in a matter of hours. From all the developed skills in the RoboCup 3D Soccer Simulation League, running is still considerably relevant to determine the winner of any match. However, current approaches do not make full use of the robotic soccer agents’ potential. To narrow this gap, we propose a way of leveraging the Proximal Policy Optimization using the information provided by the simulator for official RoboCup matches. To do this, our algorithm uses a mix of raw, computed and internally generated data. The final result is a sprinting and a stopping behavior that work in tandem to bring the agent from point a to point b in a very short time. The sprinting speed stabilizes at around 2.5 m/s, which is a great improvement over current solutions. Both the sprinting and stopping behaviors are remarkably stable.