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As mentioned in JuliaReinforcementLearning/ReinforcementLearning.jl#234 we had some strange behaviour. This was from the log_p`_a having a different size than log_p, (1, N) compared to (N,), creating a matrix for the ratio which was then reduced down.
I also found that the entropy loss was not defined to work with multi dimensional actions correctly, it was missing a multiple of how many dimensions there were in the actions space. Then it was also missing a division by 2 in one of the terms compared to how the entropy is defined on https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Differential_entropy