Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience.

Reinforcement learning is all about making decisions sequentially. In simple words, we can say that the output depends on the state of the current input and the next input depends on the output of the previous input.

Various Practical applications of Reinforcement Learning –

• This can be used in robotics for industrial automation.
• This can be used in machine learning and data processing
• This can be used to create training systems that provide custom instruction and materials according to the requirement of students.

Here in this repository I have implemented many Reinforcement techniques for different use cases and real life computation scenarios. Following are the different implementations:

• Upper Confidence Bound
• Thompson Sampling

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