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I try to solve a navigation problem with PPO; my actions space have three-part:

  1. robot linear velocity that is in [-3,3] range (getting from a tanh activation func)
  2. robot linear angular that is in [-pi/6, pi/6] range (getting from a tanh activation func)
  3. robot step-time duration that is from [0.2, 0.5, 0.8] (getting from a softmax activation func)

The problem that I face is how to calculate the ratio of probability from this separate disturbing? Mean or sum? or was there another way to calculate log_prob from different distributions? Something like log_prob from multivariable distribution!

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It depends on how you have modelled the probability distributions that you are sampling from when you turn the neural network output into a specific action.

If your three action dimensions are sampled independently (which would be a standard way to model a policy like this), then the probabilities or probability densities multiply. Which means the log probabilities add.

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  • $\begingroup$ I got my actions from 3 different output layers of actor-network, and each one has its own mean and std. So I must use the sum function to add log_prob and save it in the memory. $\endgroup$
    – m031n
    Aug 20 at 17:53

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