# What is the purpose of argmax in the PPO algorithm?

I'm kinda new to machine learning and still not too solid on math and particularly calculus. I'm currently trying to implement PPO algorithm as described in the spiningUp website :

This line is giving me a hard time :

What does the $$\operatorname{argmax}$$ mean, in this context? They are also talking about updating the policy with a gradient ascent. So, is taking argmax with respect to $$\theta$$ the same as doing:

where $$J$$ is the min() function?

In this case yes, $$J$$ is the big $$\min$$ expression and you apply Adam on that. But be careful because they say they do ascent, but automatic differentiation software usually minimizes given function so your $$J$$ would be $$−\min(⋅)$$.