EvoJAX: Hardware-Accelerated Neuroevolution

We present EvoJAX, a scalable, general purpose, hardware-accelerated neuroevolution toolkit.

Building on top of the JAX library, our toolkit enables neuroevolution algorithms to work with neural networks running in parallel across multiple TPU/GPUs.

EvoJAX achieves very high performance by implementing the evolution algorithm, neural network and task all in NumPy, which is compiled just-in-time to run on accelerators.

During the session, we'll provide extensible examples of EvoJAX for a wide range of tasks, including supervised learning, reinforcement learning and generative art.

Since EvoJAX can find solutions to most of these tasks within minutes on a single accelerator, compared to hours or days when using CPUs, we believe our toolkit can significantly shorten the iteration time of conducting experiments for researchers working with evolutionary computation.

Join us to learn more about EvoJAX: http://wandb.me/jax