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Reinforcement Learning and Deep RL Python (Theory and Projects)

You're reading from Reinforcement Learning and Deep RL Python (Theory and Projects) A complete guide to reinforcement and deep reinforcement learning in Python with theory and projects

Product type Video

Published in Sep 2022

Publisher Packt

ISBN-13 9781804610626

Length 14hrs 16mins

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

AI Sciences

View More author details

Table of Contents (13) Chapters

1. Introduction to the Course FREE CHAPTER

2. Motivation and Applications

3. Terminologies of RL

4. Naive Random Solution

5. RL-Based Q-Learning

6. Hyper Parameters and Concepts

7. SARSA (State–Action–Reward–State–Action)

8. DNN Foundation for Deep RL

9. Deep RL DQN

10. Stable Baselines Cartpole Solution

11. Trading Bot RL

12. Car Racing Game

13. Interview Prep

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Authors (1)

AI Sciences

AI Sciences

AI Sciences is a group of experts, PhDs, and practitioners of AI, ML, computer science, and statistics. Some of the experts work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. They have produced a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science. Initially, their objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory. Today, they also publish more complete courses for a wider audience. Their courses have had phenomenal success and have helped more than 100,000 students master AI and data science.

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