PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPU

Trevor McInroe; Samuel Garcin

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arXiv · 2502.00021

PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPU

Trevor McInroe·Samuel Garcin

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Abstract

We present PixelBrax, a set of continuous control tasks with pixel observations. We combine the Brax physics engine with a pure JAX renderer, allowing reinforcement learning (RL) experiments to run end-to-end on the GPU. PixelBrax can render observations over thousands of parallel environments and can run two orders of magnitude faster than existing benchmarks that rely on CPU-based rendering. Additionally, PixelBrax supports fully reproducible experiments through its explicit handling of any stochasticity within the environments and supports color and video distractors for benchmarking generalization. We open-source PixelBrax alongside JAX implementations of several RL algorithms at github.com/trevormcinroe/pixelbrax.

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Trevor McInroe, Samuel Garcin. 2025-01-16. PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPU. https://arxiv.org/abs/2502.00021

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