Reinforcement Learning with Python Explained for Beginners

Learn reinforcement learning from scratch.

Key Features

Gain an understanding of all theoretical concepts related to reinforcement learning
Master learning models such as model-free learning, Q-learning, temporal difference learning
Model the uncertainty of the environment, environment stochastic policies, and environment value functions

What You Will Learn

Understand the motivation for reinforcement learning
Understand all the elements of a Markov Decision Process
Learn how to model uncertainty of the environments
Solve Markov Decision Processes
Implement temporal difference learning and Q-learning in Python
Execute the Frozenlake project using the OpenAI Gym toolkit

About

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