# Should I just use exploitation after I have trained the Q agent?

When using a trained Q-learning algorithm in an actual game, would I just use exploitation and no longer use exploration? Should I use exploration only during the training phase?

Once you have estimated the $$Q$$ function, you can derive the policy from it in different ways. For example, you can act greedily with respect to it (see this answer), which can be formally denoted as
$$\pi(s) = \operatorname{argmax}_{a^*}Q(s, a), \; \forall s \in \mathcal{S}$$ where $$Q(s, a)$$ is your estimated value function and $$\pi$$ the policy greedily derived from it.
Of course, if your policy is not optimal, you may not want to always execute the greedy action. In that case, you could still perform some form of exploration (e.g. with the $$\epsilon$$-greedy policy).