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Book Overview & Buying
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Table Of Contents
Reinforcement Learning with Python Explained for Beginners
By :
Reinforcement Learning with Python Explained for Beginners
By:
Overview of this book
Although introduced academically decades ago, the recent developments in the field of reinforcement learning have been phenomenal. Domains such as self-driving cars, natural language processing, healthcare industry, online recommender systems, and so on have already seen how RL-based AI agents can bring tremendous gains.
This course will help you get started with reinforcement learning first by establishing the motivation for this field and then covering all the essential topics, such as Markov Decision Processes, policy and rewards, model-free learning, temporal difference learning, and so on.
Each topic is accompanied by exercises and complementing analysis to help you gain practical and tangible coding skills.
By the end of this course, not only will you have gained the necessary understanding to implement RL in your projects but also implemented an actual Frozenlake project using the OpenAI Gym toolkit.
All resources and code files are placed here: https://github.com/PacktPublishing/Reinforcement-Learning-with-Python-Explained-for-Beginners
Table of Contents (12 chapters)
Introduction to Course and Instructor
Motivation Reinforcement Learning
Terminology of Reinforcement Learning
GridWorld Example
Markov Decision Process Prerequisites
Elements of Markov Decision Process
More on Reward
Solving Markov DP
Value Approximation
Temporal Differencing - Q Learning
TD Lambda
Project Frozenlake (Open AI Gym)
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