Reinforcement Learning

• Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence.

• It allows machines and software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance. Simple reward feedback is required for the agent to learn its behaviour; this is known as the reinforcement signal.

• Reinforcement learning, in the context of artificial intelligence, is a type of dynamic programming that trains algorithms using a system of reward and punishment.

• A reinforcement learning algorithm, or agent, learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly.

• The agent learns without intervention from a human by maximizing its reward and minimizing its penalty.

• Reinforcement learning is an approach to machine learning that is inspired by behaviorist psychology. It is similar to how a child learns to perform a new task.

• As an agent, which could be a self-driving car or a program playing chess, interacts with its environment, receives a reward state depending on how it performs, such as driving to destination safely or winning a game. 

• Conversely, the agent receives a penalty for performing incorrectly, such as going off the road or being checkmated.

• The agent over time makes decisions to maximize its reward and minimize its penalty using dynamic programming. 

• The advantage of this approach to artificial intelligence is that it allows an AI program to learn without a programmer spelling out how an agent should perform the task.

        

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