What is a key feature of the actor in actor-critic models?

In actor-critic models, the key feature of the actor component is that it learns to make decisions based on the rewards received for the actions it chooses. The actor is responsible for selecting actions in a given state, aiming to maximize the expected reward over time. It does this by updating its policy based on the success or failure of past actions as indicated by the rewards received.

By focusing on the rewards from the environment, the actor refines its action-selection strategy, gradually improving its performance. This learning mechanism is crucial in reinforcement learning paradigms, where the effectiveness of an action can only be assessed after it has been executed and its consequences observed.

While the critic side of these models evaluates the actions performed and helps to provide feedback, it is the actor's role to continuously adapt its approach based on the reward signals it receives, thereby learning to choose better actions in the future based on past experiences.