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

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Miguel Abreu (Author) Other; Luís Paulo Reis (Author) FEUP; Nuno Lau (Author) Other

Conference proceedings International

Title: RoboCup 2019: Robot World Cup XXIII [Sydney, NSW, Australia, July 8, 2019].

Pages: 3-15

Indexing

Scopus - 8 Citations

Other information

Authenticus ID: P-00R-ESC

DOI: 10.1007/978-3-030-35699-6_1

Abstract (EN): 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. © 2019, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13