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<!DOCTYPE html>
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<meta name="description" content="Soccer behavior learning in parallel environments">
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<meta name="keywords" content="Reinforcement Learning, Robotics, Simulation, PPO">
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<title>DNT-RL: Soccer Behavior Learning in Parallel Environments</title>
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<h1 class="title is-1 publication-title">DNT-RL: Soccer Behavior Learning in Parallel Environments</h1>
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<div class="is-size-5 publication-authors">
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<span class="author-block">
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Macha Meijer<sup>1,2</sup>,</span>
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<span class="author-block">
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Gijs de Jong<sup>1,2</sup>,</span>
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<span class="author-block">
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Joshua Rosenthal<sup>1,2</sup>
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</span>
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</div>
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<div class="is-size-5 publication-authors">
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<span class="author-block"><sup>1</sup>University of Amsterdam,</span>
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<span class="author-block"><sup>2</sup>Dutch Nao Team</span>
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<span>Paper</span>
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<span>arXiv</span>
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<a href="https://github.com/IntelligentRoboticsLab/dnt-rl"
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<h2 class="subtitle has-text-centered">
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<span class="ddnt-rl">DNT-RL</span> learning a walk to point policy with 64 parallel agents.
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<h2 class="title is-3">Abstract</h2>
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<p>
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Reinforcement Learning (RL) has shown significant potential in the RoboCup Soccer leagues, for example in
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the SPL.
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</p>
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<p>
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Unlike tranditional hand-crafted behaviors, the usage of RL enables the development a more flexible
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behavior engine, allowing for a more dynamic gameplay.
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However, training RL behaviors is resource-intensive, both in terms of training time and the development
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time of each behavior.
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</p>
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<p>
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In this paper, we introduce t <span class="ddnt-rl">DNT-RL</span>, a framework designed for the efficient
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training of 2D RL behaviors
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using parallel training with multiple environments. The framework integrates a physics-based simulation
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with a standardized way of training behaviors, supporting parallelized and GPU-accelerated training to
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reduce the training time.
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We show the effectiveness of DNT-RL through several behaviors that are trained using the framework, which
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have been succesfully deployed in RoboCup tournaments. These results show the potential of the framework
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for development of scalable and usable RL-based behavior training.
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<h2 class="title is-3">Related Links</h2>
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<p>
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There's a lot of excellent work that was introduced around the same time as ours.
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</p>
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<p>
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<a href="https://arxiv.org/abs/2412.09417v2">Reinforcement Learning Within the Classical Robotics Stack: A
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Case Study in Robot Soccer</a> introduces
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an
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idea similar to our classic robotics stack enhanced with RL behavior.
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<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
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<pre><code>@article{meijer2025dntrl,
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author = {Meijer, Macha and de Jong, Gijs and Rosenthal, Joshua},
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title = {DNT-RL: Soccer Behavior Learning in Parallel Environments},
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journal = {% TODO}
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year = {2025},
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}</code></pre>
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Website template borrowed from <a href="https://github.com/nerfies/nerfies.github.io">NeRFies</a> and <a
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href="https://genesis-embodied-ai.github.io">Genesis</a>.
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