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This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise.

This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms Key FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical ...

Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.

About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment.

About the book Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques.

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.

This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym.

Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques.