Reinforcement Learning with Python Explained for Beginners

Description

This course begins with establishing the motivation for reinforcement learning and then progresses on to equipping you with all the necessary theory. Each section of the course helps you not only understand the fundamentals of RL but also gain necessary coding skills by taking you through exercises. By the end of the course, you will be able to complete a project using the OpenAI Gym toolkit.

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

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

This course is designed for beginners in the field of data science and machine learning. Anyone who wants to learn RL and apply it in realistic projects would benefit from this course.

Through carefully designed modules, simple-to-understand theory, engaging hands-on exercises, and realistic implementations of Reinforcement Learning (RL) in projects, this course will help you master RL. This course covers the most advanced and up-to-date methods in RL.

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

https://github.com/PacktPublishing/Reinforcement-Learning-with-Python-Explained-for-Beginners

AI Sciences are a group of experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.

1. Introduction to Course and Instructor

2. Motivation Reinforcement Learning

3. Terminology of Reinforcement Learning

4. GridWorld Example

5. Markov Decision Process Prerequisites

6. Elements of Markov Decision Process

7. More on Reward

8. Solving MDP

9. Value Approximation

10. Temporal Differencing-Q Learning

11. TD Lambda

12. Project Frozenlake (Open AI Gym)