rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer
This work provides tools to facilitate research and competition in robot soccer using reinforcement learning, but it is incremental as it builds on existing simulation and benchmark approaches.
The authors introduced an open-source simulator and framework for reinforcement learning in small robot soccer leagues, demonstrating the capabilities and limitations of two state-of-the-art methods in this context.
Reinforcement learning is an active research area with a vast number of applications in robotics, and the RoboCup competition is an interesting environment for studying and evaluating reinforcement learning methods. A known difficulty in applying reinforcement learning to robotics is the high number of experience samples required, being the use of simulated environments for training the agents followed by transfer learning to real-world (sim-to-real) a viable path. This article introduces an open-source simulator for the IEEE Very Small Size Soccer and the Small Size League optimized for reinforcement learning experiments. We also propose a framework for creating OpenAI Gym environments with a set of benchmarks tasks for evaluating single-agent and multi-agent robot soccer skills. We then demonstrate the learning capabilities of two state-of-the-art reinforcement learning methods as well as their limitations in certain scenarios introduced in this framework. We believe this will make it easier for more teams to compete in these categories using end-to-end reinforcement learning approaches and further develop this research area.