Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF 3rd ed. Edition

I’ve read more than half of the book and tried the codes, and I must say it’s a great resource for solving problems with deep reinforcement learning. While I’ve read other theory-heavy books, applying the algorithms on my own has been quite challenging. This book, however, provides the essential building blocks, allowing you to build upon them as needed. If you’re looking to apply deep RL, I highly recommend it. Plus, it comes with a colorful PDF, which is a nice bonus.

A very helpful reference.

This book is a cool intro to Reinforcement Learning, with a useful round-up of key concepts in the domain as well as applications, some of which are very much of the moment (e.g. RL agent models for web search/navigation, with Deep Research newly prominent, or e.g. trading agents in finance).Some of the material (such as Markov machines) will be familiar from CS 101, but are moved through crisply to relate them to chains of observation-action-reward, which are surely key to understanding how to state a problem as an RL problem. The book might benefit from an early discussion of the fact that system states in applications are rarely finite/countable, with most being embedded, which might motivate the reader interested in neural networks—just to ward off possible confusions. The conceptual discussions early in the book sometime veer off course, but can be useful for thinking through the nature of a well-constrained environment and reward specification.It was useful to see code, and the book delivers. A standardized schema of [class Environment] and [class Agent] with Agent making choices and querying Environment for the reward makes clear the basic schema for an RL system. The book uses CartPole as a running: a cart contains a standing pole with the pole at some angle that is usually not 90 upon initialization, so the pole is likely to fall. The Agent can nudge the cart a set amount to the left or right, with incremental reward if the pole is still standing up, no penalty if the pole is allowed to fall over, but more accumulated reward if the pole stands up longer. The learning problem is to learn to hold the stick upright by exploring different actions in response to the continuous states of the stick. It's a useful toy example for illustrating the main concepts, and is revisited with various techniques thoughout the book. Atari games are another running example.The use of a fork of OpenAI's GymAPI (`import gymnasium`) makes it clear how much of the specification of RL agents & environments is compactly expressed in programming abstractions, which is great. Chapter 3 covers all the necessary items for making an agent differentiable—pytorch tensors and optimizers—which is everything you need to get started.The book really excels at demonstrating swift deployment of the framework, with a chapter on Stock Trading Using RL (10), TextWorld agents/environments for text-based interactive fiction, and robot/drone navigation with continuous action parameters. Due attention is given to exploitation/exploration tradeoffs in these settings, Various RL methods demonstrated on the running CartPole example—Policy Gradients (Ch 11), the Cross-Entropy Method (subsampling of "the good" as a target distribution, Ch 4), Actor vs Critic Methods (Ch 12), Q-learning (Ch. 5 & 6), all using `gymnasium` as an abstraction base. Along the way, you'll learn about NLP, encoder-decoder architectures, RNNs, and transformers, and the current version of the book has a section using ChatGPT, which is swapped in as an agent for playing the text game. The Web Navigation section (Ch 14) could benefit from from an update on LLM web information retrieval, since that seems like the current drift of the technology (as opposed to browser automation).All in all this guide is a good mix of practical and conceptual material, with quick deployment a priority. I haven't used reinforcement learning until now but I may start, with a good toolkit and knowledge of where to go to get deeper, into each topic. The worked examples are great and theoretical discussions (e.g. Q-learning) having a good amount of meat.

The book is very well written and surprisingly it reads very quickly. I'm about 80% of the way through my first read. The code snippets are very useful and the github page is well mantained. The only thing: for this being called "Deep reinforcement learning" I was expecting there to be more exercises. As mock exercises I've been reading the theory and trying to implement models before looking at the code. There are some suggestions at the end of some chapters about how one could take the notions discussed in the chapter forwards. I also appreciated that the Author cites research papers that I can go read if I want to delve deeper into specific topics.All in all, pretty good book on RL, can highly advise for someone looking for the technicalities.

Maxim Lapan's Deep Reinforcement Learning Hands-On is one of the most practical introductions to modern deep reinforcement learning (DRL) techniques available today. Blending clarity with hands-on experience, the book offers an outstanding balance between theoretical foundations and real-world implementation. With LLMs and AI Agent use cases becoming more complex, DRL is an essential technique to build robust Gen AI apps, and this book goes a long way in helping it.The structure of the book is particularly effective: starting with core concepts like Markov Decision Processes and Q-learning, it gradually progresses to advanced topics such as policy gradients, actor-critic methods, Proximal Policy Optimization (PPO), and deep exploration strategies. Each chapter builds logically on the previous one, making complex topics feel manageable and intuitive.What sets this book apart is its strong focus on PyTorch-based implementation. Lapan doesn't just explain algorithms — he walks readers through clean, well-commented code examples that reinforce learning through practice. Readers not only understand how algorithms work but also how to build them from scratch, a critical skill for researchers and practitioners alike.Code is also available on github to easily fork and experiment with.

I started reading Deep Reinforcement Learning Hands-On (Third Edition) because I wanted to go beyond surface-level machine learning and deepen my understanding of how agents actually learn and adapt. The book does a great job starting with simple environments and basic concepts like rewards and actions, and then steadily builds toward more complex topics like reinforcement learning with human feedback (RLHF), MuZero, discrete optimization, and multi-agent systems.While the examples in the book are mainly classic RL environments, the concepts can easily be connected to real-world challenges — like designing recommendation systems, planning healthcare treatments with delayed outcomes, or building strategies in business decision-making.At over 600 pages, the book covers a lot of ground. It could easily have been split into two volumes (introductory and advanced RL), but following the full journey helped me appreciate the layered complexity of learning systems.Highly recommended for anyone starting with reinforcement learning or looking to move into advanced areas like autonomous systems and strategic AI design