Coming from a process (optimal) control background, I have begun studying the field of deep reinforcement learning.
Sutton & Barto (2015) state that
particularly important (to the writing of the text) have been the contributions establishing and developing the relationships to the theory of optimal control and dynamic programming
With emphasis on the elements of reinforcement learning - that is, policy, agent, environment, etc., what are the key differences between (deep) RL and optimal control theory?
In optimal control we have, controllers, sensors, actuators, plants, etc, as elements. Are these different names for similar elements in deep RL? For example, would an optimal control plant be called an environment in deep RL?