Alternatives

Best Grokking Deep Reinforcement Learning alternatives in 2026

Grokking Deep Reinforcement Learning is practical introduction to deep reinforcement learning with exercises. These 9 alternatives come from the same rankings, ordered by editorial score. Connected products carry a small, disclosed lift.

9 alternatives From best reinforcement learning and edge ML books How we rank Share

Cover of Grokking Deep Reinforcement Learning

Ranked alternatives

  1. Reinforcement Learning: An Introduction

    Has a free option. The standard free text for reinforcement learning, with the full PDF from the authors and exercises throughout. It focuses on core ideas rather than the newest deep reinforcement learning systems.

    Best for Standard free reinforcement learning textbook

    PDF, PrintFree

  2. Reinforcement Learning and Optimal Control

    Has a free option. A rigorous dynamic programming view of reinforcement learning, with a free 2025 PDF and lectures on the author's site. It is heavier on mathematics than Sutton and Barto, so it suits graduate study.

    Best for Graduate study of RL and dynamic programming

    PDFFree

  3. TinyML

    The most practical guide here for running models on microcontrollers, with a free preview. The full book is paid and has little theory, unlike the Sze book.

    Best for Running machine learning on microcontrollers

    PDF, PrintPaid

  4. Bandit Algorithms

    Has a free option. A free PDF from the authors and a Cambridge University Press text on bandit theory, the simplest setting where exploration matters. It is theory-first and narrower than Sutton and Barto, so it ranks below that standard text.

    Best for Theory of exploration and bandit problems

    PDF, PrintFree

  5. Efficient Processing of Deep Neural Networks

    A structured guide to hardware and algorithm choices for efficient deep neural network processing, from a 2020 Springer book. It is paid, has no free text, and covers no reinforcement learning.

    Best for Hardware for efficient deep neural networks

    PDF, PrintPaid

  6. Reinforcement Learning: Theory and Algorithms

    Has a free option. A free, frequently updated theory text that treats sample-efficient reinforcement learning carefully. It has little code and is still in progress, so it ranks below the more finished textbooks.

    Best for Theory-first study of reinforcement learning

    PDFFree

  7. Foundations of Deep Reinforcement Learning

    Pairs deep reinforcement learning theory with Python implementations, with editions from 2019 to 2021. It has more theory than the Grokking book and less breadth than Sutton and Barto, so it sits between them.

    Best for Theory and Python for deep RL

    PrintPaid

  8. Algorithms for Reinforcement Learning

    Has a free option. A free, short survey of reinforcement learning algorithms from 2010 that reads quickly. It predates deep reinforcement learning, so it ranks below the newer textbooks, though the PDF is free.

    Best for Short overview of classic RL algorithms

    PDF, PrintFree

  9. Fringe Learning: Resource-Efficient RL for Edge ML*

    Is a one-time purchase. A low-priced PDF on reinforcement learning for microcontrollers and NPUs, which fills the edge gap in this list. It is narrower than the textbooks and has no ratings or reviews on its listing.

    Best for Reinforcement learning on edge hardware

    PDFOne-time purchase

About Grokking Deep Reinforcement Learning alternatives

What is the best alternative to Grokking Deep Reinforcement Learning?
On listme.name the highest-scored alternative is Reinforcement Learning: An Introduction, followed by Reinforcement Learning and Optimal Control. The right choice depends on price, platform and whether you need open source.
Is there a free alternative to Grokking Deep Reinforcement Learning?
Yes. Free options include Reinforcement Learning: An Introduction, Reinforcement Learning and Optimal Control, Bandit Algorithms.

Made by, or connected to, the publisher of this site or people the publisher works with. Listed on the same criteria as everything else, with the lift described in How we rank and Disclosure.