How to choose action in TD(0) learning

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I am currently reading Sutton's Reinforcement Learning: An introduction book. After reading chapter 6.1 I wanted to implement a TD(0) RL algorithm for this setting:

enter image description here enter image description here

To do this, I tried to implement the pseudo-code presented here: enter image description here

Doing this I wondered how to do this step A <- action given by π for S: I can I choose the optimal action A for my current state S? As the value function V(S) is just depending on the state and not on the action I do not really know, how this can be done.

I found this question (where I got the images from) which deals with the same exercise - but here the action is just picked randomly and not choosen by an action policy π.

Edit: Or this is pseudo-code not complete, so that I have to approximate the action-value function Q(s, a) in another way, too?

temporal-difference reinforcement-learning
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