EvoJAX
A scalable, hardware-accelerated neuroevolution toolkit built on JAX for parallel training across TPUs/GPUs.
What is EvoJAX?
EvoJAX is a hardware-accelerated neuroevolution toolkit that enables evolutionary algorithms to train neural networks in parallel across multiple TPUs or GPUs. It solves the problem of slow evolution experiments by compiling the entire pipeline into optimized JAX code, drastically reducing training time from days to minutes.
Researchers and practitioners in evolutionary computation, machine learning, and AI who need to run large-scale neuroevolution experiments efficiently, particularly those with access to GPU/TPU hardware.
Developers choose EvoJAX for its exceptional performance gains through hardware acceleration, modular design that supports easy extension, and proven scalability across diverse tasks from control problems to generative art.
Overview
EvoJAX is a general-purpose neuroevolution toolkit designed to leverage hardware acceleration for evolutionary algorithms. Built on JAX, it compiles the entire evolution pipeline—algorithms, neural networks, and tasks—into optimized NumPy code that runs in parallel across multiple TPUs or GPUs, achieving significant speedups over CPU-based implementations.
Key Features
- Hardware Acceleration — Compiles evolution algorithms, neural networks, and tasks into JAX for parallel execution on TPUs/GPUs.
- Scalable Neuroevolution — Enables large-scale evolution experiments that complete in minutes instead of hours or days.
- Modular Framework — Provides interchangeable components for algorithms, policy networks, and tasks, allowing easy extension.
- Diverse Task Support — Includes examples for supervised learning, reinforcement learning, control tasks, and generative art.
- Multi-Agent Training — Supports training of multiple agents in complex environments like WaterWorld.
Philosophy
EvoJAX aims to make evolutionary computation practical for a wide range of tasks by leveraging modern hardware accelerators, moving beyond single-task optimizations to a general, scalable toolkit.
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