Reinforcement learning is a niche machine learning technique that has surfaced in the last few years. In context-based decision making, reinforcement learning helps the machine take action-provoking decision making through a trial-and-error approach to achieve the optimal algorithmic model for a situation. DATAVERSITY brought this topic to us in their article, “The Fundamentals of Deep Reinforcement Learning.”
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective or maximize along a particular dimension over many steps; for example, maximizing the points won in a game over many moves. They can start from a blank slate, and under the right conditions they achieve superhuman performance. These algorithms are penalized when they make the wrong decisions and rewarded when they make the right ones – this is reinforcement.
Reinforcement algorithms that incorporate deep learning are the state of the art and progressing rapidly. They are expected to eventually achieve many goals in the real world.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.
