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[HELP] - How to pass multineat.Genome weights to CMA-ES or other optimization algorithms #567

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@itsron143

Hello!

I have a few questions about implementing a learner in the modular robot evolution cycle for evolving the brain and body (evolution + learning). The learner would be used to optimize the brain parameters for a body and then continue with the evolution of the robot (brain + body)

I'm unable to understand how to pass the weights of multineat.Genome of the brain as parameters to CMA-ES.

Following example 4e in revolve2/examples, I was able to understand how to get the weights from cpg_network_structure as parameters to CMA-ES and then convert it back to a BrainCpgNetworkStatic brain using uniform_from_params() method.

How would the weights of BrainCpgNetworkNeighborV1 be extracted from multineat.Genome to pass it as parameters to CMA-ES and also then develop the BrainCpgNetworkNeighborV1 brain using the updated parameters?

Any help would be appreciated!

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