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.

翻译：基于像素的强化学习常受限于三维渲染环境的性能与复杂性。研究人员不得不在高速但底层的引擎与较慢但更易用的Python框架之间进行权衡。为解决此问题，我们推出了PyBatchRender——一个用于高吞吐量批量三维渲染的Python库，在简单场景下可实现超过每秒一百万帧的渲染速度。该库基于Panda3D游戏引擎构建，在利用其成熟生态系统的同时，通过优化的批量渲染技术实现高达1000倍的性能提升。PyBatchRender专为基于像素的强化学习设计，作为物理无关的渲染器，它比专用库更具灵活性，比典型的游戏引擎封装更易配置，其速度可与Madrona等先进C++引擎相媲美。用户完全使用Python即可通过数十行代码创建自定义场景，从而为可扩展的AI训练实现快速原型开发。本库开源且易于集成，旨在为研究人员和开发者普及高性能三维仿真技术。项目地址：https://github.com/dolphin-in-a-coma/PyBatchRender。