A high-level structural overview of classical Reinforcement Learning algorithms

Reinforcement Learning (RL) is an increasing subset of Machine Learning and one of the most important frontiers of Artificial Intelligence, since it has gained great popularity in the last years with a lot of successful real-world applications in robotics, games and many other fields. It denotes a set of algorithms that handle sequential decision-making and have the ability to take intelligent decisions depending on their local environment.

A RL algorithm can be described as a model that indicates to an agent which set of actions it should take within a closed environment in order to to maximize a predefined overall reward. Generally speaking, the agent tries different sets of actions, evaluating the total obtained return. After many trials, the algorithm learns which actions give a greater reward and establishes a pattern of behavior. Thanks to this, it is able to tell the agent which actions to take in every condition.

The goal of RL is to capture more complex structures and use more adaptable algorithms than classical Machine Learning, infact RL algorithms are more dynamic in their behavior compared to classical Machine Learning ones.

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