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Book Overview & Buying
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Table Of Contents
Reinforcement Learning and Deep RL Python (Theory and Projects)
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Reinforcement Learning and Deep RL Python (Theory and Projects)
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Overview of this book
Reinforcement learning is a subset of machine learning. In the RL training method, desired actions are rewarded, and undesired actions are punished. Deep RL is also a subfield of machine learning. In deep RL, intelligent machines and software are trained to learn from their actions in the same way that humans learn from experience. Deep RL has the capability to solve complex problems that were unmanageable by machines in the past. Therefore, the potential applications of deep RL in various sectors are enormous.
We will start with an introduction to reinforcement learning and look at some case studies and real-world examples. Then you will look at Naïve/Random solutions and RL-based solutions. Next, you will see different types of RL solutions such as hyperparameters, Markov Decision Process, Q-Learning, and SARSA followed by a mini project on Frozen Lake. You will then learn deep learning/neural networks and deep RL/deep Q networks. Next, you will work on car racing and trading projects. Finally, you will go through some interview questions.
By the end of this course, you will be able to relate the concepts and practical applications of reinforcement and deep reinforcement learning with real-world problems and implement any project that requires reinforcement and deep reinforcement learning knowledge from scratch.
All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Reinforcement-Learning-and-Deep-RL-Python-Theory-and-Projects-
Table of Contents (13 chapters)
Introduction to the Course
Motivation and Applications
Terminologies of RL
Naive Random Solution
RL-Based Q-Learning
Hyper Parameters and Concepts
SARSA (State–Action–Reward–State–Action)
DNN Foundation for Deep RL
Deep RL DQN
Stable Baselines Cartpole Solution
Trading Bot RL
Car Racing Game
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