How to code an $\epsilon$-soft policy for on-policy Monte Carlo control?

I was trying to code the on-policy Monte Carlo control method. The initial policy chosen needs to be an $$\epsilon$$-soft policy.

Can someone tell me how to code an $$\epsilon$$-soft policy?

I know how to code the $$\epsilon$$-greedy. In $$\epsilon$$-soft, there are inequalities in place of equalities which is the issue for coding the $$\epsilon$$-soft.

You cannot code an $$\epsilon$$-soft policy directly, because it is not specific enough.

A policy is $$\epsilon$$-soft provided that there is at least a probability of $$\frac{\epsilon}{|\mathcal{A}|}$$ for choosing any action, where $$\mathcal{A}$$ is the set of all possible actions.

I know how to code the $$\epsilon$$-greedy.

Then you already know how to code the most commonly-used $$\epsilon$$-soft policy, because an $$\epsilon$$-greedy policy is an $$\epsilon$$-soft policy.

there are inequalities in place of equalities which is the issue for coding the $$\epsilon$$-soft

That is correct. In fact $$\epsilon$$-soft can be thought of as a constraint or test. So you could write some code that tested whether any policy was an $$\epsilon$$-soft policy for any given value of $$\epsilon$$. Or you could write code that determined the value of $$\epsilon$$ for any policy.

Slightly harder would be code that forced a supplied policy to meet constraints of being $$\epsilon$$-soft, because adjusting any low probabilities to be high enough would mean reducing other probabilities, and there are multiple ways you could do that.

However, a really simple way to make any starting policy $$\pi$$ into an $$\epsilon$$-soft variant is to make the policy choice in 2 steps - first step choose between the original policy with probability $$(1-\epsilon)$$, and with probability $$\epsilon$$ choose a fixed policy with equal probability for each action. Second step, evaluate whichever policy the first step chose to determine the action.