Python Reinforcement Learning, Deep Q-Learning and TRFL

About this course

Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from data centre energy saving (cooling data centres) to smart warehousing solutions.

This course covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You will be introduced to the concept of Reinforcement Learning, its advantages and why it's gaining so much popularity. This course also discusses on Markov Decision Process (MDPs), Monte Carlo tree searches, dynamic programmings such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will learn to build convolutional neural network models using TensorFlow and Keras. You will also learn the use of artificial intelligence in a gaming environment with the help of OpenAI Gym.

By the end of this course, you will explore reinforcement learning and will have hands-on experience with real data and artificial intelligence (AI) to build intelligent systems.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

• Lauren Washington is currently the Lead Data Scientist and Machine Learning Developer for smartQED, an AI driven start-up. Lauren worked as a Data Scientist for Topix, Payments Risk Strategist for Google (Google Wallet/Android Pay), Statistical Analyst for Nielsen, and Big Data Intern for the National Opinion Research Center through the University of Chicago. Lauren is also passionate about teaching Machine Learning. She’s currently giving back to the data science community as a Thinkful Data Science Bootcamp Mentor and a Packt Publishing technical vi

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