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What is Reinforcement Learning ?

Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation.
What is Reinforcement Learning ?

Reinforcement Learning (RL) is the closed form of learning to the way a human being learns. It consists of an intelligent agent that interacts with its environment smartly to reap a numerical reward. The goal of the agent is to learn sequential actions so as to maximize the long time reward. Like a human being who learns from his experience with the real world, keep exploring new things and updating his values and beliefs, the RL agents works on the similar principle to maximize his own rewards in the long run. In 2017, Google’s AlphaGo computer program used RL to beat the world champion in the game of Go.


Various Practical applications of Reinforcement Learning –

  • RL can be used in robotics for industrial automation.
  • RL can be used in machine learning and data processing
  • RL can be used to create training systems that provide custom instruction and materials according to the requirement of students.
An introduction to Reinforcement Learning
by Thomas Simonini Reinforcement learning is an important type of Machine Learning where an agentlearn how to behave in a environment by performing actions and seeing theresults. In recent years, we’ve seen a lot of improvements in this fascinating area ofresearch. Examples include DeepMind and…

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