I am reading Sutton and Barto's book on reinforcement learning. I thought that reward and return were the same things.
However, in Section 5.6 of the book, 3rd line, first paragraph, it is written:
Whereas in Chapter 2 we averaged rewards, in Monte Carlo methods we average returns.
What does it mean? Are rewards and returns different things?
Return refers to the total discounted reward, starting from the current timestep.
As the accepted answer states, the return at the current timestep is equal to the sum of discounted rewards from all future timesteps until the end of the episode. In Chapter 5 of Sutton and Barto, returns must be used to estimate the state-value and action-value functions because episode lengths are unrestricted and may be greater than one. In contrast, Chapter 2 deals with the very special case of multi-armed bandits in which episode lengths are always equal to one: The agent begins each episode in a fixed start state, takes an action, receives a reward, and then the episode terminates and the agent begins the next episode at the same start state. Therefore, a return is equivalent to a reward in Chapter 2 because all episodes have length one.
