RobocupGym: A challenging continuous control benchmark in Robocup
This provides a new benchmark for researchers in robotics and reinforcement learning, though it is incremental as it builds on existing simulation tools.
The authors tackled the lack of diverse and applicable robotics benchmarks by introducing RobocupGym, a reinforcement learning environment based on Robocup's 3D simulation league, which simplifies applying RL to high-dimensional continuous control tasks in robotic football.
Reinforcement learning (RL) has progressed substantially over the past decade, with much of this progress being driven by benchmarks. Many benchmarks are focused on video or board games, and a large number of robotics benchmarks lack diversity and real-world applicability. In this paper, we aim to simplify the process of applying reinforcement learning in the 3D simulation league of Robocup, a robotic football competition. To this end, we introduce a Robocup-based RL environment based on the open source rcssserver3d soccer server, simple pre-defined tasks, and integration with a popular RL library, Stable Baselines 3. Our environment enables the creation of high-dimensional continuous control tasks within a robotics football simulation. In each task, an RL agent controls a simulated Nao robot, and can interact with the ball or other agents. We open-source our environment and training code at https://github.com/Michael-Beukman/RobocupGym.