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When reading pytorch tutorial:
Our aim will be to train a policy that tries to maximize the discounted, cumulative reward
Rt0=∑∞t=t0γt−t0rt, whereRt0is also known as the return
I know γ is the discount factor, but I am not sure that what t-t0 ofγt−t0 mean?
Thank you.
I have no experience with reinformcement learning, however looking at the figure I think I understand what is meant. Gamma is the discount factor, which is taken to the power t-t0, i.e. the number of episodes starting from t. This gives the discount factor for a specific episode, which is then multiplied by the return of that episode, r_t, to get the discounted reward for one specific episode. The total return is then computed by summing all the future rewards for future episodes.
