Udacity Deep Reinforcement Learning Nanodegree Review 2026

Last updated: September 2026. Written by Josh Hutcheson, OnlineCourseing editor. Program details checked on Udacity’s own page on 22 September 2026. See our review methodology.

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Overview

Reinforcement learning (RL) is the branch of machine learning in which an agent learns by trial and error, taking actions in an environment and adjusting its behavior to maximize a reward. Deep reinforcement learning combines this with neural networks, which lets agents handle problems too large for lookup tables: playing Atari games from raw pixels, controlling robot arms, or coordinating several agents at once. It is the technique behind well-known results such as DeepMind’s AlphaZero, which the program covers as a case study.

Udacity’s Deep Reinforcement Learning Nanodegree (program code nd893) is built around that progression. It starts with the mathematical framework of RL, moves through the two big families of methods, value-based and policy-based, and ends with multi-agent learning. Each stage closes with a graded project reviewed by a person. This review is based on Udacity’s current program page, which we read in full on 22 September 2026, together with what recent students report.

The program at a glance

Syllabus

The core of the program is four courses. The hours below are Udacity’s own estimates.

1. Introduction to Deep Reinforcement Learning (34 hours)

The longest course lays the foundations. You learn to frame a task as a Markov decision process, then implement the classic solution methods yourself: Monte Carlo control, used to teach an agent to play Blackjack, and temporal-difference methods such as SARSA, Q-learning and Expected SARSA. A mini project has you solve the Taxi task in OpenAI Gym, and a final lesson shows how to adapt these algorithms to continuous state spaces, which sets up the move to neural networks.

2. Value-Based Methods (10 hours)

Here the neural networks arrive. You extend Q-learning with deep Q-networks and experience replay. Project: Navigation. You train an agent to move through a large world, collecting yellow bananas while avoiding blue ones.

3. Policy-Based Methods (31 hours)

Instead of learning the value of each action, policy-based methods optimize the policy directly. You cover policy gradient methods, Proximal Policy Optimization (PPO), implemented by training an agent to play Atari Pong, and actor-critic methods, taught by Miguel Morales, which combine the value-based and policy-based approaches. An optional lesson applies deep RL to the optimal execution of portfolio trades. Project: Continuous Control. You train a double-jointed arm to reach target locations.

4. Multi-Agent Reinforcement Learning (8 hours)

The final core course introduces multi-agent RL and studies AlphaZero as a case study. Project: Collaboration and Competition. You train a pair of agents to play tennis against each other.

Optional courses

• Special Topics in Deep RL (8 hours): dynamic programming, the classic first step toward the RL problem.
• Neural Networks in PyTorch (6 hours): a refresher on neural networks, convolutional networks and PyTorch, useful if your deep learning is rusty.
• Computing Resources (20 minutes): how to use Udacity’s browser-based workspaces.
• C++ Programming (19 hours): C++ basics and optimization, with a histogram-filter optimization exercise. It reads as if it has been borrowed from Udacity’s self-driving car programs, and it is not needed for the RL projects.

Read the full syllabus on Udacity →

Prerequisites

Udacity rates the program Advanced and lists five prerequisites: intermediate Python, proficiency with a deep learning framework, neural network basics, object-oriented programming basics and reinforcement learning fundamentals. In practice that means you should be able to build and train a small neural network in PyTorch without following a tutorial line by line. The optional PyTorch course helps if you are rusty, but it is a six-hour refresher, not a first course.

If you are not there yet, Udacity’s Deep Learning Nanodegree (Intermediate, about 50 hours, 4.7 from 992 reviews, updated September 2026) is the natural first step; see our Udacity Deep Learning Nanodegree review. If you need broader machine learning foundations, our review of the Intro to Machine Learning with TensorFlow Nanodegree covers the entry-level option.

Costs and duration

The program no longer has its own fixed monthly fee. When we checked Udacity’s page on 22 September 2026 there were three ways to pay:

Udacity discounts these prices often, and on the day we checked a time-limited sale was cutting them by almost half. The duration drives the value. The 83 core hours take about two months at 10 hours a week, which makes the monthly subscription the cheapest route for a steady learner. The one-time purchase makes sense only if you expect to take much longer. Training agents can also take real compute time, and one reviewer complained that the GPU hours provided were too few to tune hyperparameters comfortably, so plan to use your own GPU or a cloud notebook as well. Current codes are on our Udacity coupon page, and our guide to Udacity Nanodegree costs compares the plans.

RISK CHECK

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Pros and cons

Who should enroll, and how to prepare

Enroll if you already train neural networks in PyTorch, you want to work on robotics, game AI, recommendation or control problems, and you learn best by building agents and getting feedback on them. The three projects give you working DQN, continuous-control and multi-agent agents to show in a portfolio.

Look elsewhere if you are still learning what a neural network is, you want a credential from a university, or you need to learn the newest RL libraries rather than the classic algorithms. The alternatives below cover each of those cases.

How to prepare. Before you start, make sure you can write a training loop in PyTorch, explain what a loss function and a gradient are, and read basic probability notation, since Markov decision processes are defined in those terms. Set up a Python environment with current versions of PyTorch and Gymnasium on day one, so that version mismatches in the course code surface early rather than in the middle of a project. At 10 hours a week, aim to finish the foundations course in about a month; the last three courses move faster.

Is the content up to date?

This is the main question to settle before enrolling. Udacity marks the page as updated in August 2026, but the student reviews on it point the other way. In June 2025 Edgar Maucourant wrote that “the content is great although a bit dated on some topic,” with videos that sometimes refer to material from other modules. In April 2024 Angela G was far harsher, complaining of “Pytorch codes from 6 years ago” and GitHub repositories referenced in the videos that have since changed.

The syllabus itself offers a small, checkable example. It still asks you to solve “OpenAI Gym’s Taxi-v2 task.” OpenAI Gym is now maintained as Gymnasium by the Farama Foundation, and Gymnasium’s own documentation lists the current version of that environment as Taxi-v4. None of this changes the underlying algorithms, which are the point of the program, but you should expect to update library versions and fix small incompatibilities as you go.

About the instructors

Udacity lists nine instructors, among them Alexis Cook and Cezanne Camacho (curriculum leads), Mat Leonard (listed as a senior data engineer at Octave), Miguel Morales, who teaches the actor-critic lessons, Luis Serrano, PhD, an ML engineer and quantum AI research scientist, Arpan Chakraborty, computational physicist Juan Delgado, and content developers Chhavi Yadav and Dana Sheahan. The team is large, so the teaching style varies from lesson to lesson, which some reviewers notice.

Student reviews

Udacity’s page shows an average of 4.6 out of 5 from 357 reviews. The most recent ones are mixed in a consistent way: students value the breadth and the projects, and criticize the age of the materials.

• “Great course to understand modern algorithms in RL.” (Andrei, 5 stars, September 2024)
• “Amazing course with the most advanced knowledge about deep RL, yet presented in so pleasant way.” (Agnieszka L, 5 stars, November 2023)
• “The content is great although a bit dated on some topic … overall the experience is good and the exercise helps a lot.” (Edgar Maucourant, 4 stars, June 2025)
• “The course explains even the heavy concepts very easily.” (Anuj P, 4 stars, August 2023)
• A one-star review from Angela G (April 2024) called the code outdated, the actor-critic videos hard to follow and the practical work limited to video-game environments, while praising the papers and repositories the course points to.

Alternatives to the Udacity Deep RL Nanodegree

Figures from each provider’s own page, checked on 22 September 2026:

The University of Alberta specialization is the strongest alternative for learners who care most about understanding why the algorithms work; it is the most theory-focused of the options here. The Hugging Face course is the best free option and uses up-to-date tooling. Udacity’s advantage is the reviewed projects. For a wider comparison, see our ranking of the best reinforcement learning courses.

Compare the Coursera RL Specialization →

Frequently asked questions

Is the Udacity Deep Reinforcement Learning Nanodegree still available in 2026?

Yes. When we checked on 22 September 2026 the program page (nd893) was live, marked as last updated on 10 August 2026, and rated 4.6 out of 5 from 357 reviews.

How long does the Deep Reinforcement Learning Nanodegree take?

Udacity lists about 83 hours for the four core courses: 34 hours of foundations, 10 of value-based methods, 31 of policy-based methods and 8 of multi-agent RL. At 10 hours a week that is about two months, and the optional courses add up to roughly 33 more hours.

How much does it cost?

It is included in the Udacity subscription, listed at $249 a month or $212 a month if you prepay four months, and it can be bought on its own for a one-time $999 list price. Udacity runs frequent sales, so check the live price before paying.

What are the prerequisites?

Udacity lists intermediate Python, proficiency with a deep learning framework, neural network basics, object-oriented programming basics and reinforcement learning fundamentals. The program is rated Advanced; if neural networks are new to you, take a deep learning course first.

What projects are in the program?

Three graded projects: Navigation (a deep Q-network agent that collects yellow bananas and avoids blue ones), Continuous Control (training a double-jointed arm to reach targets) and Collaboration and Competition (training two agents to play tennis). There is also a mini project on the Taxi task in OpenAI Gym.

Is the code up to date?

Partly. Several reviewers since 2024 say the PyTorch code and some videos are dated, and the outline still names OpenAI Gym’s Taxi-v2 task, while Gymnasium, the maintained successor to Gym, is now on Taxi-v4. Expect to adapt some code to current library versions.

Is the Deep Reinforcement Learning Nanodegree worth it?

It is worth it if you want structured, project-based practice with the core deep RL algorithms and can finish in about two months on the subscription. If you mainly want the theory, or want current tooling, the Coursera Reinforcement Learning Specialization and the free Hugging Face Deep RL Course are strong alternatives.

Conclusion

The Udacity Deep Reinforcement Learning Nanodegree remains a solid, well-sequenced way to learn deep RL by building agents: you implement the classic methods, then DQN, PPO and actor-critic, and finish with multi-agent tennis. Its three reviewed projects are what you pay for. Go in knowing the code shows its age, and plan to finish in about two months on the subscription. If rigorous theory or current tooling matters more to you than project feedback, start with the University of Alberta specialization or the free Hugging Face course instead.

See the Deep RL Nanodegree on Udacity →

Related: every Udacity Nanodegree in 2026 · our complete Udacity Nanodegree review · Udacity Generative AI Nanodegree review · Udacity Agentic AI Nanodegree review