My 1st Reinforcement Learning program

The basket(agent) doesn’t know anything about the game initially. The goal is to catch all the apples. Using reinforcement learning, it learns in 350 iterations and plays like an expert. If the basket catches the falling apple, the score increases. Otherwise, the score resets. The number of iteration/ episodes and the score can be seen at the top right corner.  (In the video you can see it learning initially by exploring options, and after 43 sec(from 350 to 500 iterations), it has learned everything).  Github link: https://github.com/asawaswapnil/intellegent-basket .