Is Reinforcement Learning: An Introduction better than Reinforcement Learning: Theory and Algorithms?

Reinforcement Learning: An Introduction ranks higher in our list of reinforcement learning and edge ML books (#1 against #7, score 94.0 against 86.0). Reinforcement Learning: An Introduction is best for: standard free reinforcement learning textbook. Reinforcement Learning: Theory and Algorithms is best for: theory-first study of reinforcement learning.

Which is cheaper, Reinforcement Learning: An Introduction or Reinforcement Learning: Theory and Algorithms?

Reinforcement Learning: An Introduction: Full PDF free from the authors; printed copies sold separately. Reinforcement Learning: Theory and Algorithms: Free PDF, updated frequently; no print edition listed.

Comparison · Reinforcement learning and edge ML books

Reinforcement Learning: An Introduction vs Reinforcement Learning: Theory and Algorithms: which is better in 2026?

Reinforcement Learning: An Introduction94.0#1 in best reinforcement learning and edge ML booksReinforcement Learning: Theory and Algorithms86.0#7 in best reinforcement learning and edge ML books

Reinforcement Learning: An Introduction ranks higher in our list of reinforcement learning and edge ML books (#1 against #7, score 94.0 against 86.0).

How we rank Share

94.0
Editorial score
86.0
2
Formats
1
Yes
Free to use
Yes

Which should you choose?

Choose Reinforcement Learning: An Introduction if you want

Standard free reinforcement learning textbook.

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.

Choose Reinforcement Learning: Theory and Algorithms if you want

Theory-first study of reinforcement learning.

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.

Side by side

	Reinforcement Learning: An Introduction	Reinforcement Learning: Theory and Algorithms
Score	94.0	86.0
Rank in list	#1 of 10	#7 of 10
Price	FreeFull PDF free from the authors; printed copies sold separately	FreeFree PDF, updated frequently; no print edition listed
Free to use	Yes	Yes
Formats	PDF, Print	PDF
Author	Richard S. Sutton and Andrew G. Barto	Alekh Agarwal, Kianté Brantley, Nan Jiang, Sham M. Kakade and Wen Sun
Level	Intermediate	Advanced
Free to read online	Yes	Yes
Latest edition	2nd edition, 2018	Draft, updated in 2026
Math required	Light to moderate: probability	Heavy: probability and proofs
Best for	Standard free reinforcement learning textbook	Theory-first study of reinforcement learning
Strengths	Full PDF free from the authors Exercises, errata and slides online Clear build-up from basic ideas	Free PDF with frequent updates Rigorous, theorem-based treatment Written by reinforcement learning researchers
Limits	Older than many recent deep reinforcement learning methods Long, with a strong focus on theory	Still in progress, not a finished edition Little code or practical advice
Links	Visit site	Visit site

Questions about Reinforcement Learning: An Introduction vs Reinforcement Learning: Theory and Algorithms

Is Reinforcement Learning: An Introduction better than Reinforcement Learning: Theory and Algorithms?: Reinforcement Learning: An Introduction ranks higher in our list of reinforcement learning and edge ML books (#1 against #7, score 94.0 against 86.0). Reinforcement Learning: An Introduction is best for: standard free reinforcement learning textbook. Reinforcement Learning: Theory and Algorithms is best for: theory-first study of reinforcement learning.
Which is cheaper, Reinforcement Learning: An Introduction or Reinforcement Learning: Theory and Algorithms?: Reinforcement Learning: An Introduction: Full PDF free from the authors; printed copies sold separately. Reinforcement Learning: Theory and Algorithms: Free PDF, updated frequently; no print edition listed.