Reinforcement learning is when we teach a robot by giving it treats or stickers when it does a good job. The robot learns what to do to get the treats and tries to get as many treats as possible by doing the right things. It's like playing a game to learn how to make good choices.
CNN and RNN are like helpers for the robot. They help the robot see and understand things better. They can look at pictures and tell the robot what's in them, like a cat or a dog, or they can help the robot understand words and sentences. They are like tools to help the robot learn even more about the world around it.
Similar to how you may have seen models trained in Reinforcement learning play games like Mario or Flappy Bird to learn how to clear levels and earn as many achievement points or treats as possible.
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