PyTorch 1.x Reinforcement Learning Cookbook: Over 60 recipes to design, develop, and deploy self-learning AI models using Python

This book is a comprehensive guide to reinforcement learning techniques and algorithms using PyTorch 1.x, with real-world examples and recipes to help you master RL concepts and implement popular algorithms.

You'll learn how to design and build self-learning AI models that can solve control and optimization challenges faced by data scientists today, and apply modern RL libraries to simulate a controlled environment for your projects.

The book covers important RL concepts, implementation of algorithms in PyTorch 1.x, industry-specific applications, and problem-solving techniques such as the multi-armed bandit problem and cartpole problem using Q-learning and function approximation.

You'll also learn how to use Deep Q-Networks to complete Atari games and effectively implement policy gradients.

By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and solve real-world problems.

Don't miss out on this opportunity to master reinforcement learning techniques and become a leader in the field!