reward that's the so called reinforcement learning framework so every time step you can give it a positive reward or negative reward or baby no reward it could be a very scarce right like if you play chess just at the end of the game you give +1 for winning or -1 for losing so in the aixi framework that's completely sufficient so occasionally you give a reward signal and you ask the agent to maximize river but not greedily sort of you know the next one next one because that's very bad in the long run if you're greedy so