 In order to act near optimally, the agent must reason about the long term consequences of its actions (i.e., maximize future income), although the immediate reward associated with this might be negative.Thus, reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off.It has been applied successfully to various problems, including robot control, elevator scheduling, telecommunications, backgammon, checkers and Go (AlphaGo).