evojax 0.1.0.post7

EvoJAX: Hardware-accelerated Neuroevolution.

EvoJAX: Hardware-Accelerated Neuroevolution

This repository contains the implementation of EvoJAX, a toolkit for accelerated Neuroevolution experiments.

Installation

EvoJAX is implemented in JAX which needs to be installed first.

Install JAX: Please first follow JAX's installation instruction with optional GPU/TPU backend support. In case JAX is not set up, EvoJAX installation will still try pulling a CPU-only version of JAX. Note that Colab runtimes come with JAX pre-installed.

Install EvoJAX:

# Install from PyPI.
pip install evojax

# Or, install from our GitHub repo.
pip install git+https://github.com/google/evojax.git@main

Code Overview

EvoJAX is a framework with three major components, which we expect the users to extend.

1. Neuroevolution Algorithms All neuroevolution algorithms should implement the evojax.algo.base.NEAlgorithm interface and reside in evojax/algo/. We currently provide PGPE, with more coming soon.
2. Policy Networks All neural networks should implement the evojax.policy.base.PolicyNetwork interface and be saved in evojax/policy/. In this repo, we give example implementations of the MLP, ConvNet, Seq2Seq and PermutationInvariant models.
3. Tasks All tasks should implement evojax.task.base.VectorizedTask and be in evojax/task/.

These components can be used either independently, or orchestrated by evojax.trainer and evojax.sim_mgr that manage the training pipeline. While they should be sufficient for the currently provided policies and tasks, we plan to extend their functionality in the future as the need arises.

Examples

As a quickstart, we provide non-trivial examples (scripts in examples/ and notebooks in examples/notebooks) to illustrate the usage of EvoJAX. We provide example commands to start the training process at the top of each script. These scripts and notebooks are run with TPUs and/or NVIDIA V100 GPU(s):

Supervised Learning Tasks

• MNIST Classification - We show that EvoJAX trains a ConvNet policy to achieve >98% test accuracy within 5 min on a single GPU.
• Seq2Seq Learning - We demonstrate that EvoJAX is capable of learning a large network with hundreds of thousands parameters to accomplish a seq2seq task.

Classic Control Tasks

• Locomotion - Brax is a differentiable physics engine implemented in JAX. We wrap it as a task and train with EvoJAX on GPUs/TPUs. It takes EvoJAX tens of minutes to solve a locomotion task in Brax.
• Cart-Pole Swing Up - We illustrate how the classic control task can be implemented in JAX and be integrated into EvoJAX's pipeline for significant speed up training.

Novel Tasks

• WaterWorld - In this task, an agent tries to get as much food as possible while avoiding poisons. EvoJAX is able to learn the agent in tens of minutes on a single GPU. Moreover, we demonstrate that multi-agents training in EvoJAX is possible, which is beneficial for learning policies that can deal with environmental complexity and uncertainties.
• Abstract Paintings (notebook 1 and notebook 2) - We reproduce the results from this computational creativity work and show how the original work, whose implementation requires multiple CPUs and GPUs, could be accelerated on a single GPU efficiently using EvoJAX, which was not possible before. Moreover, with multiple GPUs/TPUs, EvoJAX can further speed up the mentioned work almost linearly. We also show that the modular design of EvoJAX allows its components to be used independently -- in this case it is possible to use only the ES algorithms from EvoJAX while leveraging one's own training loops and environment implantation.

Disclaimer

This is not an official Google product.