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I have a question about implementing policy gradient methods for problems with continuous action spaces.

Assume that actions are sampled from a diagonal Gaussian distribution with mean vector $\mu$ and standard deviation vector $\sigma$. As far as I understand, we can define a neural network that takes the current state as the input and returns a $\mu$ as its output. According to OpenAI Spinning Up, the standard deviation $\sigma$ can be represented in two different ways:

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I don't completely understand the first method. Does it mean that we must set the log standard deviations to fix numbers? Then how do we choose these numbers?

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  • $\begingroup$ If you don't get an answer meanwhile, it may be a good idea to look at existing implementations (e.g. on Github). $\endgroup$ – nbro Jul 19 at 12:36

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