Basics of Reinforcement Learning AI Engineers Must Know Of

Machine learning (ML) or ML-powered algorithms are getting deployed in every business sector you can think of, these days. The role of ML and artificial intelligence (AI) in the functioning of our society has been on an unprecedented high off late, with new innovations getting discovered each day. From finance to healthcare, to travel, to manufacturing, AI and ML are being deployed across industry sectors, and benefitting them heavily.

If the second half of the 20th century got known for the impressive progress in web connectivity and computing power, 21st century is getting dominated by the emergence of a plethora of disruptive technologies such as ML and AI.

What Do We Mean by Reinforcement Learning?

Humans are great at solving an array of challenging issues, ranging from low-level motor control activities (running, playing football, walking) to high-level perceptive tasks (doing poetry, writing, conversation, mathematics). The technique of reinforcement learning allows hardware/software to imitate human behavior by leveraging computer algorithms that are specially designed for the said purpose.

In a more formal language, reinforcement learning deals in the development of goal-specific algorithms that help learn a machine on how to accomplish a sophisticated objective, or how to optimize the chances of attaining a predetermined result.

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