Hands-On Reinforcement Learning with Python

Discount factor

We have seen that an agent goal is to maximize the return. For an episodic task, we can define our return as R_t= r_t+1 + r_t+2 + ..... +r_T, where T is the final state of the episode, and we try to maximize the return R_t.

Since we don't have any final state for a continuous task, we can define our return for continuous tasks as R_t= r_t+1 + r_t+2+....,which sums up to infinity. But how can we maximize the return if it never stops?

That's why we introduce the notion of a discount factor. We can redefine our return with a discount factor , as follows:

---(1)

---(2)

The discount factor decides how much importance we give to the future ...