Reinforcement Learning with Python

Reinforcement Learning can be achieved with the help of a Python algorithm called the Q-Learning technique. This algorithm can be achieved by following the below steps:

• Import the required libraries.

• Define and visualize the graphs.

• Define the reward system for the bot.

• Define the utility functions to be used in training.

• Train and evaluate the bot using the Q-Matrix.

• Define and visualize the new graph with the produced environmental clues.

• Define some functions for the training process.

• Visualize the environmental matrices.

• Train and evaluate the model. 

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Reinforcement Learning is the concept of Machine Learning that helps achieve Artificial Intelligence projects. It is one of the potent concepts that help develop AI bots that resemble human intelligence. You can start learning Reinforcement Learning by enrolling yourself in the courses that best suit your interest.

Reinforcement Learning is a Machine Learning concept that helps the software agents to take appropriate actions in an environment. It is also a part of Deep Learning that allows you to increase some portions of the cumulative rewards. Self-driving cars are an example of Reinforcement Learning. The autonomous driving feature is designed by learning from various driving tasks it was prone to. Then with the help of Reinforcement Learning, the trajectory was optimized, motions were planned, made it aware of drivable zones, and was trained to avoid collisions.

Yes, you will get a certificate of completion for Reinforcement Learning after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.