The objective of this tutorial is to equip students of data science and artificial intelligence with conceptual knowledge of reinforcement learning. The tutorial is of immense significance to programmers as it explains how a game scenario can be coded in Python.
Prepared by Mr. Bharani Kumar Depuru Director of 360digiTMG and alumnus of IIT and ISB this video elaborates on the concepts of Reinforcement Learning in Artificial Intelligence. Mr.Bharani Kumar Depuru has 15 plus years of experience in Data Science, Digital Transformation, Business Analytics, Artificial Intelligence, Deep Learning, IoT, and Project Management.
Reinforcement Learning is a paradigm in Machine Learning which enables the software to take suitable action to maximize rewards in a particular scenario. Reinforcement learning is also called Unsupervised Learning because the machine is only loaded with training data. The machine has to learn from its experience and choose the best possible course of action. Examples: Clustering, feature learning, density estimation
At the other end of the spectrum is Supervised learning. In Supervised Learning, the training data is loaded with an answer key. In this manner, the model is trained with the correct answer. Examples: Classification, regression, object detection, and image captioning.
Typically in reinforcement learning an agent receives information about its environment and chooses actions to maximize rewards. Ex - A neural network that " looks" at a video game screen and outputs games actions in order to maximize its score trained by reinforcement learning.
This means we have an agent that needs to be trained over a specific period of time to interact with a specific environment and improves its performance with a period of time with regard to the type of action it performs on the environment.
In this video, the main steps of a reinforcement learning method are delineated and explained with suitable examples. The examples of an interactive chess game and the cart- pole problem are very lively and interesting.
The most captivating portion of this tutorial is the live programming of a reinforcement learning application in Python. The instructor guides the audience through the various steps of code generation.
This is a must-watch for students of artificial intelligence who wish to use python to script their reinforcement learning-based applications.

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