# PPO: sampling next action vs picking the most probable action

According to the original Proximal Policy Optimization paper (PPO paper), we always sample an action from the actor distribution.

The overall loss is calculated as $$\text{loss} = pg_\text{loss} - \text{entropy} \times \text{ent}_\text{coef} + vf_\text{loss} \times vf_\text{coef}$$, which includes entropy maximization, which intuitively encourages the exploration by encouraging the action probability distribution to be more chaotic.
Why do we explicitly force our agent to over-explore? While I understand why we do that for the first $$m$$ epochs, I do not see a reason why it is always done instead of choosing the best action (in case of discrete action space) for the last $$N-m$$ epochs?