PyBatchRender: A Python Library for Batched 3D Rendering at Up to One Million FPS
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Abstract (arXiv)
Reinforcement learning from pixels is often bottlenecked by the performance and complexity of 3D rendered environments. Researchers face a trade-off between high-speed, low-level engines and slower, more accessible Python frameworks. To address this, we introduce PyBatchRender, a Python library for high-throughput, batched 3D rendering that achieves over 1 million FPS on simple scenes. Built on the Panda3D game engine, it utilizes its mature ecosystem while enhancing performance through optimized batched rendering for up to 1000X speedups. Designed as a physics-agnostic renderer for reinforcement learning from pixels, PyBatchRender offers greater flexibility than dedicated libraries, simpler setup than typical game-engine wrappers, and speeds rivaling state-of-the-art C++ engines like Madrona. Users can create custom scenes entirely in Python with tens of lines of code, enabling rapid prototyping for scalable AI training. Open-source and easy to integrate, it serves to democratize high-performance 3D simulation for researchers and developers. The library is available at https://github.com/dolphin-in-a-coma/PyBatchRender.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 3 Jan 2026.
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References · 7
- 2025Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Successcited by 844
- 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 281
- 2024ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AIcited by 216
- 2021ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrationscited by 193
- 2025MuJoCo Playgroundcited by 57
- 2025AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real Worldcited by 46
- 2021Large Batch Simulation for Deep Reinforcement Learningcited by 18
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