evosax

Introduction to evosax: JAX-Based Evolution Strategies 🦎

Evosax is a powerful tool designed to simplify the implementation of Evolution Strategies (ES) for neuroevolution applications, leveraging the capabilities of JAX. Whether you're facing challenges with asynchronous processes or looking to harness the power of massive vectorization and high-throughput accelerators, evosax can be a game-changer. By utilizing JAX's XLA compilation and Auto-vectorization/Parallelization features, evosax scales ES efficiently to work with your favorite accelerators.

Key Features

• Streamlined ES Cycle: Evosax follows the classical Evolution Strategies cycle using the ask, evaluate, and tell methods. This approach ensures compatibility with JAX's jit, vmap/pmap, and lax.scan, making operations smoother and faster.

• Vast Strategy Library: The library includes many classic and modern neuroevolution strategies. These include popular methods like CMA-ES, Differential Evolution, OpenAI-ES, and Augmented Random Search, among others.

Getting Started with evosax

Here is a simple example of using evosax for a simple ES implementation:

import jax
from evosax import CMA_ES

# Initialize the search strategy
rng = jax.random.PRNGKey(0)
strategy = CMA_ES(popsize=20, num_dims=2, elite_ratio=0.5)
es_params = strategy.default_params
state = strategy.initialize(rng, es_params)

# Implement the ask-evaluate-tell loop
for t in range(num_generations):
    rng, rng_gen, rng_eval = jax.random.split(rng, 3)
    x, state = strategy.ask(rng_gen, state, es_params)
    fitness = ...  # Evaluate the population
    state = strategy.tell(x, fitness, state, es_params)

# Retrieve the best member and its fitness
state.best_member, state.best_fitness

This sample illustrates the simplicity of implementing ES with evosax, featuring easy initialization and operation via the ask-evaluate-tell loop. Users define their fitness evaluation function and integrate it seamlessly with the framework.

Supported Evolution Strategies

Evosax boasts a comprehensive collection of Evolution Strategies:

• OpenAI-ES: Based on the work of Salimans et al. (2017), it is widely known for its effectiveness in deep neuroevolution applications.

• CMA-ES and Variants: Strategies like CMA-ES, Sep-CMA-ES, BIPOP-CMA-ES, and IPOP-CMA-ES are present for robust covariance matrix adaptations.

• Genetic Algorithms: They include Simple Genetic, SAMR-GA, among others, focusing on evolutionary computation techniques.

• Modern ES Variants: Evosax covers strategies like PGPE, ARS, ESMC, and more recent advancements like Noise-Reuse ES and Diffusion Evolution.

• Particle Swarm and Differential Evolution: These strategies are inspired by natural phenomena and are included for solving complex optimization problems.

Each strategy is well-documented, with examples provided to help users get started quickly. Links to Colab notebooks provide easy access to runnable code examples for each strategy.

Conclusion

Evosax serves as a versatile platform for implementing a myriad of Evolution Strategies to tackle complex neuroevolution problems. Its integration with JAX makes it a valuable tool for researchers and developers interested in leveraging state-of-the-art computing power to solve challenging optimization problems efficiently and effectively. With evosax, users have access to a comprehensive suite of strategies and powerful computational capabilities, setting a new standard for ES implementations.