Reinforcement Learning

Reinforcement learning is a subfield of machine learning that is concerned with making decisions in an unknown environment to achieve a specific objective.

History of Reinforcement Learning

The history of reinforcement learning dates back to the 1950s when researchers were exploring ways to simulate animal behavior using computers. In the early 1960s, a psychologist named Richard Sutton proposed using temporal difference learning to model how animals learn. In the late 1970s, Sutton and his colleague Andrew Barto developed the first reinforcement learning algorithm called TD(0). This algorithm used temporal difference learning to learn a value function that estimates the expected payoff of each state in a Markov decision process.

Applications of Reinforcement Learning

Over the years, researchers in reinforcement learning have developed a wide range of algorithms and techniques that have been used in various applications, such as:

• Game playing
• Robotics
• Autonomous systems.

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