The optimal action of the agent at each step is the action that maximizes the Q-function, Q(s, a), which denotes the long-term reward of choosing action a in state s. In practice, we learn this Qfunction using a parametric function, Q(s, a; θ), where θ is the model parameter vector.
the predicted optimal action of the agent is the action that maximizes Q(s, a; θ).