9 Best Reinforcement Learning Courses in 2026 (Free and Paid)

Last updated: November 2026. Written by Josh Hutcheson, OnlineCourseing editor. See our review methodology.

QUICK VERDICT

Reinforcement learning (RL) is the branch of machine learning where an agent learns by acting: it tries something, receives a reward or penalty, and adjusts its policy to collect more reward over time. It is the method behind game-playing systems such as AlphaGo, a standard tool in robotics and operations research, and, through reinforcement learning from human feedback (RLHF), part of how today’s chatbots are aligned.

RL is also the least beginner-friendly corner of machine learning, so the course you start with matters. We loaded every course below on 2 November 2026 and recorded its rating, enrollment, cost and last update. The list covers the structured university path, implementation-first video courses, the best free material, and two options with instructor support. Free resources are included on merit; we earn nothing from them.

Best reinforcement learning courses at a glance

The 9 best reinforcement learning courses in 2026

1. Reinforcement Learning Specialization, University of Alberta (Coursera): best overall

This four-course specialization from the University of Alberta, taught by Martha and Adam White, is the closest thing the field has to a standard curriculum. It follows Sutton and Barto’s Reinforcement Learning: An Introduction, and Alberta is where Richard Sutton has long led RL research. You work through bandits, Markov decision processes, dynamic programming, Monte Carlo methods, temporal-difference learning, Q-learning, Dyna, function approximation and policy gradients, then build a complete agent in the capstone.

On 2 November 2026 Coursera listed it at 4.7 stars from 3,606 course reviews with 68,095 learners enrolled, and estimated two months to complete at 10 hours a week. It runs on Coursera’s subscription (Coursera Plus is $239 a year, normally $399) and you can preview each course’s first module before paying. Coursera also offers financial aid on many of its courses.

Best for: anyone who wants to understand why RL algorithms work, not only how to call them. Skip if: you want deep RL with neural networks straight away; the specialization spends most of its time on tabular and linear methods.

Not ready for four courses? The first one, Fundamentals of Reinforcement Learning, stands alone (4.8 stars from 2,914 reviews) and covers bandits, MDPs and dynamic programming.

2. Hugging Face Deep Reinforcement Learning Course: best free course

Hugging Face’s Deep RL Course is the most current free introduction to deep RL. You train agents with Deep Q-Learning, policy gradients, actor-critic methods and PPO, the algorithm family used in RLHF, on environments from classic control to Atari-style games, then publish your trained agents to the Hugging Face Hub, where they double as portfolio evidence.

It is free, self-paced and has no deadlines. Completing 80% of the assignments earns a free certificate of completion and 100% earns a certificate of honors, which makes it the only free option on this list with a certificate attached. It is lighter on theory than Alberta, so it works best as the deep RL half of a plan that starts with classical RL.

Best for: learners who want hands-on deep RL with current tools at no cost. Skip if: you have never seen an MDP; do some classical RL first.

3. Artificial Intelligence: Reinforcement Learning in Python (Udemy): best for coding it yourself

Lazy Programmer’s course is the implementation counterpart to Alberta. You write epsilon-greedy bandits, dynamic programming, Monte Carlo, SARSA, Q-learning and approximation methods in plain Python and NumPy, so by the end each algorithm is code you wrote rather than a library call. On 2 November 2026 it was rated 4.9 from 10,824 ratings with 50,356 students and had been updated in August 2026.

It assumes comfortable Python and NumPy, and it is a one-time purchase that Udemy frequently discounts, with Udemy’s refund window on courses bought directly. The same instructor’s deep RL sequel is pick 7.

Best for: programmers who learn by implementing. Skip if: you want a university credential or graded feedback.

4. CS 285 Deep Reinforcement Learning, UC Berkeley: best free advanced course

Sergey Levine’s CS 285 is the deep RL course researchers recommend most often, and the spring 2026 site shows how current it is: homework covers imitation learning, policy gradients, Q-learning and actor-critic, offline RL, and a new LLM RL assignment. Berkeley publishes the lecture slides and homework, and the full Fall 2023 lecture recordings are free on the course site.

There is no certificate and no grading for outside learners, and Levine assumes you already know RL and deep learning basics. Reddit’s r/reinforcementlearning regulars make the same point: the lectures are excellent, but they are not an introduction.

Best for: learners who finished a foundations course and want research-level deep RL. Skip if: you are new to RL or to neural networks.

5. XCS224R Deep Reinforcement Learning (Stanford Online): best credential

Stanford’s XCS224R is the professional version of its graduate deep RL course: 100% online, with course facilitators, assignments graded to a 70% pass mark, and a Stanford Certificate of Achievement that counts toward the Artificial Intelligence Professional Program. Tuition was $2,045 on 2 November 2026, with sessions starting 2 November 2026 (12 weeks, including Stanford’s winter closure) and 24 May 2027.

Expect 10 to 15 hours a week. Course materials stay available for 90 days after the course ends. It is the most expensive option here, and the right one when an employer is paying or you need a recognizable credential with deadlines that keep you moving.

Best for: working engineers who want structure, support and a Stanford certificate. Skip if: you are paying yourself and only need the knowledge; Berkeley’s free material covers similar ground.

6. Deep Reinforcement Learning Nanodegree (Udacity): best for graded projects

Udacity’s Deep RL Nanodegree (nd893) is an advanced, project-based program: 83 hours across 8 courses, 35 lessons and 3 projects covering value-based methods, policy gradients and REINFORCE, actor-critic methods, multi-agent training, Markov games and AlphaZero. Udacity listed it at 4.6 stars from 357 ratings and showed an update dated 4 October 2026. Its value is human review of your projects, which free courses cannot offer.

Udacity sells it on subscription; today that is $150 a month (normally $249). It lists intermediate Python, a deep learning framework, neural network basics and RL fundamentals as prerequisites, so it fits after picks 1 or 3, not before. Our Udacity Deep Reinforcement Learning Nanodegree review goes through the projects in detail.

Best for: learners who want reviewed projects for a portfolio. Skip if: you need the fundamentals first.

7. Advanced AI: Deep Reinforcement Learning in Python (Udemy): best deep RL follow-on

Once tabular methods make sense, Lazy Programmer’s deep RL sequel adds neural networks: deep Q-networks, policy gradients and actor-critic agents trained on OpenAI Gym-style environments. It was rated 4.7 from 6,562 ratings with 45,910 students and updated in February 2026 when we checked. It pairs naturally with pick 3 and costs far less than the instructor-led options.

8. David Silver’s UCL Reinforcement Learning lectures: best free theory

Before leading the AlphaGo project at DeepMind, David Silver taught reinforcement learning at UCL. His ten 2015 lectures, from the introduction and MDPs through policy gradients, exploration and RL in classic games, remain one of the clearest free treatments of RL theory, and the slides and Easy21 assignment are on his teaching page. They predate PPO and RLHF, and there is no certificate or feedback, but the foundations they teach have not changed.

9. Reinforcement Learning from Human Feedback, DeepLearning.AI: fastest RLHF primer

If your interest in RL comes from large language models, this free short course explains RLHF in 1 hour 32 minutes: the preference and prompt datasets, the training process, and how to evaluate a tuned model against its base. Google Cloud’s Nikita Namjoshi teaches it with code examples that tune Llama 2. It is a primer, not an RL course, and works best after you know what a policy and a reward model are.

Why reinforcement learning matters for your career

RLHF is the stage that turns a raw language model into a helpful assistant, and it moved RL from robotics and game labs into mainstream AI work. Alignment, fine-tuning and agent teams now ask for RL literacy, and Berkeley’s decision to add an LLM RL assignment to CS 285 in 2026 shows how central it has become. Outside LLMs, RL drives robot control, recommendation and ad systems, inventory and logistics optimization, and chip design research.

RL rarely appears alone in a job description. It shows up next to deep learning, PyTorch and research experience, and raises the ceiling of ML roles rather than defining one. For the broader foundation, see our machine learning courses guide and best deep learning courses.

Prerequisites, honestly

Before any course on this list, you want comfortable Python (you can write a class and debug a loop without help), basic probability (expected value, conditional probability), some linear algebra, and ideally one machine learning course. Deep RL adds neural networks and a framework, and most current material uses PyTorch. If those pieces are missing, start with our data science courses and come back; RL will make far more sense.

Classical RL or deep RL: which course type do you need?

Searches split between “reinforcement learning” and “deep reinforcement learning” courses, and the difference decides which course fits. Classical RL (bandits, MDPs, dynamic programming, Monte Carlo, temporal-difference learning) teaches the ideas on problems small enough to reason about; that is the Alberta specialization, Silver’s lectures and Lazy Programmer’s first course. Deep RL replaces lookup tables with neural networks so the same ideas scale to images and continuous control: DQN, policy gradients, actor-critic and PPO, covered by Hugging Face, Berkeley, Stanford, Udacity and the Udemy sequel.

Do the classical half first. Deep RL without it turns into hyperparameter roulette, because you cannot tell whether an agent fails from a bug, a reward design problem or an unstable algorithm.

The path we’d follow

• Weeks 1–2: David Silver’s first four lectures (free) for the conceptual map.
• Months 1–3: the Alberta specialization as your spine, or Lazy Programmer’s RL in Python if you learn by implementing. Running both in parallel works well: theory from one, code from the other.
• Months 3–4: the Hugging Face Deep RL course for modern deep RL and free notebooks, with agents published to your Hub profile.
• Months 4–6: Berkeley CS 285 lectures and homework if you are heading toward research; Udacity or Stanford if you want graded projects and a credential.
• Then: one original environment. Wrapping a problem you care about in a Gymnasium interface and training an agent on it is the project that turns coursework into interview material.

Where RL skills get used

Robotics and autonomous systems are the classic home of RL; our robotics courses guide covers the adjacent skills. Recommendation and ad systems use RL-style methods to decide what to show next. Operations teams apply it to scheduling, inventory and routing. Quant researchers test it for execution and portfolio problems, with the caveat that retail RL trading bots mostly lose money; our algorithmic trading courses guide is more realistic about that. And LLM teams use RLHF and related preference-optimization methods to align models and train agents.

Free reinforcement learning resources worth bookmarking

These are not courses, but every learner on this list ends up using them:

• Sutton and Barto, Reinforcement Learning: An Introduction (2nd edition): the standard textbook, free to read online from Richard Sutton’s site. The Alberta specialization follows it chapter by chapter.
• OpenAI Spinning Up: a free deep RL guide with clear explanations of policy optimization, a curated list of key papers and reference implementations of PPO, SAC and TD3.
• Gymnasium: the maintained successor to OpenAI Gym and the standard environment interface (version 1.4.0, October 2026). Most current courses use it.
• Stable-Baselines3: reliable PyTorch implementations of the main algorithms (version 2.9.2, October 2026), useful for checking your own code against a known-good baseline.

How we chose these courses

We started from the courses learners search for and recommend (Bing and Google autocomplete, and threads on r/reinforcementlearning), loaded each course page on 2 November 2026, and recorded its rating, enrollment, cost, prerequisites and last update. Courses that had not been updated in years, or that covered RL only as a side topic, were dropped. Earlier versions of this page listed LinkedIn Learning RL classes and several 2019–2021 Udemy courses (a Java RL course, a Unity ML-Agents course, AI compilations); we removed them as stale or off-topic.

We rank on merit for the learner, not on commission. Some links are affiliate links and we may earn a commission if you buy through them; the free courses here earn us nothing and are included because they are among the best material available. See our review methodology for how we evaluate courses.

Reinforcement learning courses: frequently asked questions

What is the best reinforcement learning course?

For most learners, the University of Alberta’s Reinforcement Learning Specialization on Coursera: four courses built around Sutton and Barto’s textbook, rated 4.7 from 3,606 reviews with 68,095 enrolled. If you learn by coding, Lazy Programmer’s Artificial Intelligence: Reinforcement Learning in Python on Udemy (4.9 from 10,824 ratings, updated August 2026) is the strongest implementation-first course.

Can I learn reinforcement learning for free?

Yes. Hugging Face’s Deep RL course is free and even issues a free certificate of completion when you finish 80% of the assignments. UC Berkeley publishes Sergey Levine’s CS 285 lectures, David Silver’s 2015 UCL lectures are free to watch, and Sutton and Barto’s textbook is free online. Coursera also lets you preview the first module of the Alberta courses.

Is there a free reinforcement learning course with a certificate?

The Hugging Face Deep RL course is the clearest option: completing 80% of the assignments earns a free certificate of completion, and 100% earns a certificate of honors. DeepLearning.AI’s RLHF short course is free to take, though its accomplishment badge requires a paid PRO membership.

What are the prerequisites for reinforcement learning?

Comfortable Python, basic probability (expectations, distributions), some linear algebra, and ideally one machine learning course. Deep RL adds neural networks and a framework such as PyTorch. Udacity lists RL fundamentals as a prerequisite for its Nanodegree, so take a foundations course first if you are new.

How long does it take to learn reinforcement learning?

Coursera estimates about two months at 10 hours a week for the Alberta specialization. Stanford’s XCS224R expects 10 to 15 hours a week for 10 weeks (12 over the winter break), and Udacity lists 83 hours for its Nanodegree. Plan on three to six months to go from the basics to training deep RL agents on your own problems.

Is reinforcement learning still relevant in 2026?

Yes. Reinforcement learning from human feedback is part of how large language models are aligned, and Berkeley’s spring 2026 CS 285 now includes an LLM RL homework. Robotics, recommendation systems, operations research and game AI still use RL directly. It rarely appears alone in a job posting, but it is a common requirement in research and advanced ML roles.

Should I take a classical RL or a deep RL course first?

Classical first. Bandits, Markov decision processes, dynamic programming, Monte Carlo and temporal-difference learning are the ideas every deep RL algorithm builds on. Learners who start with deep RL often end up tuning hyperparameters without knowing why an agent fails. The Alberta specialization or Silver’s lectures cover the classical half.

Do I need a GPU to learn reinforcement learning?

Not for the classical material, which runs on a laptop. Deep RL on Atari-style environments benefits from a GPU, and free notebook services such as Google Colab are enough for course exercises. The Hugging Face course is built around those free notebooks.

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