Question 1

How to install evosax with GPU support in JAX?

Accepted Answer

First, install JAX with GPU support using pip or conda (e.g., pip install 'jax[cuda]'), then install evosax via pip. The README specifies Python 3.10+ and a working JAX installation as prerequisites.

Question 2

evosax vs other Python ES libraries like DEAP or pymoo?

Accepted Answer

evosax excels in hardware acceleration and scalability on GPUs/TPUs through JAX, while DEAP and pymoo are more general-purpose but lack built-in JAX integration. Choose evosax for large-scale, performance-critical optimization tasks.

Question 3

How to use evosax for reinforcement learning policy search?

Accepted Answer

Define neural network parameters as the solution vector, select an ES strategy like OpenAI-ES or ARS, and evaluate fitness by simulating environments. The README includes a reinforcement learning example notebook for training MLP control policies.

Question 4

Does evosax support parallel evaluation on multiple devices?

Accepted Answer

Yes, evosax is designed for vectorization using JAX's vmap and can leverage pmap for multi-device parallelism. The examples include a notebook on parallelization for scaling across GPUs or TPUs.

Question 5

What's the difference between CMA-ES and Differential Evolution in evosax?

Accepted Answer

CMA-ES adapts a covariance matrix for continuous optimization, while Differential Evolution uses vector differences for population-based search. Both are implemented with JAX acceleration, and the README provides separate import paths and references.

Question 6

How to implement a custom fitness function with evosax?

Accepted Answer

Define a JAX-compatible function that takes a population array and returns fitness scores, then call it in the eval step of the ask-eval-tell loop. The API allows flexible integration, as shown in the basic usage code snippets.

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