In the context of Reinforcement Learning, what does return refer to?

In Reinforcement Learning, return specifically refers to the total future cumulative reward that an agent receives after taking an action in its environment. This concept is essential because it encompasses not only the immediate reward but also all future rewards that are expected as a consequence of that action.

Return helps in evaluating the long-term success of the agent’s decisions, guiding it to favor actions that yield higher returns over time rather than merely considering immediate rewards. Typically, this is calculated using a discount factor that weighs the importance of immediate versus distant rewards, allowing the agent to make informed decisions that maximize its overall benefit in the environment.

Understanding return in this way is crucial for developing effective reinforcement learning algorithms, as agents learn to optimize their policies based on maximizing the cumulative rewards they can achieve throughout an episode or across multiple episodes of interaction with the environment.