Reinforcement learning is a method for a computer to learn how to perform a task by attempting new things and receiving rewards when it succeeds. It entails teaching an agent to make decisions in a given situation in order to maximize a reward. The agent learns through trial and error, earning positive or negative reinforcement based on its behaviors. The agent monitors the current condition of the environment and chooses an action to do at each time step. The action results in a new state and a matching reward, and the cycle continues. Reinforcement learning is used in the chess game to educate the machine each time by rewarding excellent moves and inflicting penalties for poor moves.

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Machine Learning (ML)

Machine Learning refers to a group of computer algorithms that can learn from examples and improve themselves without being explicitly coded by a human.

Deep Learning (DL)

Deep learning is a subfield of AI that uses algorithms inspired by the structure and function of the brain, called neural networks, to process and analyze even bigger amounts of data.

Q-Learning

Q-learning is a method for training artificial intelligence agents to make decisions in uncertain, dynamic environments.

Supervised and Unsupervised Learning

Both are the types of machine learning. They are the same in terms of results but different in how those are obtained.

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Reinforcement Learning (RL)

Machine Learning (ML)

Deep Learning (DL)

Q-Learning

Supervised and Unsupervised Learning

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