evojax VS evolution-strategies-starter

Awesome results! Would love to see a comparison with other accelerated evolution methods (eg https://github.com/google/evojax)

For which problems do you want to apply multiple-objective optimization? What are the objectives? https://mml-book.github.io/ is only about single objective optimization (including constraints). Multi objective reinfocement learning (https://arxiv.org/pdf/1908.08342.pdf) is not really MO-optimization. Machine learning optimization frameworks like https://github.com/google/evojax support single objective optimization and quality-diversity (MAP-elites), but not MO-optimization.

population based algos dont care about differentiability https://github.com/google/evojax https://github.com/RobertTLange/evosax https://github.com/nnaisense/evotorch https://github.com/uber-research/PyTorch-NEAT

Googles evojax project shows that evolutionary algorithms may be applied in the machine learning domain. And https://github.com/google/jax provides means to implement these algorithms to be deployed on CPUs/GPUs or even TPUs. But some questions remain unanswered:

Check out EvoJAX if you haven't seen it! Recently released for neuroevolution

Code for https://arxiv.org/abs/2202.05008 found: https://github.com/google/evojax

Found relevant code at https://github.com/openai/evolution-strategies-starter + all code implementations here

Code for https://arxiv.org/abs/1703.03864 found: https://github.com/openai/evolution-strategies-starter

Incorrect, but if anyone's curious, that paper's code is here.