Reinforcement learning is a type of machine learning paradigm where the model take action to maximize the notion of cumulative reward much like living beings do.
Learning how to play games, and self-driving cars are all modeled as a reinforcement learning problem.
If the problem to be modeled is a game, then the screen is taken as input. The algorithm takes the pixels as input and passes it through multiple layers of convolutional neural networks to give an output for the next steps to take. The outcome of the steps taken by the model serves as the positive or negative reinforcement.
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