I understand the general idea behind the Actor-Critic architecture. The actor maps state to action, and the critic maps state + action to reward.
But I don't fully understand how the critic output (reward space) can be used to train the actor sub-network.
How can a reward be used to train the actor sub-network to make it choose a better action when the actor sub-network doesn't get the reward as input or output?