RobocupGym: A challenging continuous control benchmark in Robocup

• URL: http://arxiv.org/abs/2407.14516v1
• Date: Wed, 3 Jul 2024 15:26:32 GMT
• Title: RobocupGym: A challenging continuous control benchmark in Robocup
• Authors: Michael Beukman, Branden Ingram, Geraud Nangue Tasse, Benjamin Rosman, Pravesh Ranchod,
• Abstract summary: We introduce a Robocup-based RL environment based on the open source rcssserver3d soccer server. In each task, an RL agent controls a simulated robot, and can interact with the ball or other agents.
• Score: 7.926196208425107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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