RobocupGym: A challenging continuous control benchmark in Robocup
<|reference_start|>RobocupGym: A challenging continuous control benchmark in Robocup: 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.<|reference_end|>
@article{beukman2024robocupgym:,
    title={RobocupGym: A challenging continuous control benchmark in Robocup},
    author={Michael Beukman and Branden Ingram and Geraud Nangue Tasse and
  Benjamin Rosman and Pravesh Ranchod},
    journal={arXiv preprint arXiv:2407.14516},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14516},
    primaryClass={cs.RO cs.LG}
}