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Advanced AI: Deep Reinforcement Learning in PyTorch (v2) (Udemy.com)

Build Artificial Intelligence (AI) agents using Reinforcement Learning in PyTorch: DQN, A2C, Policy Gradients, +More!

4.7
516 reviews

Created by: Lazy Programmer Inc.

Last updated February 2026

Our take

Based on the ratings of 516 students, a sample of their written reviews and the syllabus, as the course stood in February 2026. No course pays to be reviewed.

This is an advanced course from Lazy Programmer Inc. on deep reinforcement learning. It covers DQN, policy gradients and A2C, then applies DQN and A2C to Atari environments like Breakout and Pong. It runs about 15.7 hours across 66 lectures, so it is focused rather than sprawling. The requirements are real: undergraduate math, solid Python and NumPy, convolutional networks, and the RL basics. A background review section exists, but the listed requirements still expect the RL fundamentals up front.

Of 516 ratings, 373 are five stars and 129 are four, and only six are one or two stars. Reviewers praise the clear, detailed explanations, the clean notebooks that all follow one template, and quick answers from the instructor or his team. One says the hand-held treatment of the replay buffer helps nervous learners. Another says it finally made them confident writing RL code. The complaints are few: one reviewer found the explanations mechanical, and another found the tone arrogant and the code files confusing at the start.

The course was last updated in February 2026, and one reviewer calls it up to date. Another notes that people with a CS or ML degree can skip a lot of the theory lessons. The portfolio trading section is a preview only and needs the VIP tier. Newcomers should start elsewhere, but for anyone who already builds neural nets and wants a clear, code-first path into DQN and A2C, this is a solid pick.

Best forDeep learning practitioners who want to implement DQN and A2C and train Atari agents, with fast replies from the team.
Skip it ifYou are new to Python or neural nets, or want a broad survey of RL beyond DQN and A2C.

Pros

  • Clean notebooks that follow one template, so each algorithm is easy to read and repeat
  • Hand-held explanations of basics like the replay buffer help nervous learners
  • Reviewers say the instructor or his team answers questions quickly
  • Covers DQN, policy gradients and A2C, then applies them to Atari games like Breakout and Pong

Cons

  • Assumes calculus, probability, CNNs and RL basics; one reviewer says it needs about two years of prior study
  • The trading project is a preview only and needs the VIP tier, so that promise is mostly a teaser
  • A few reviewers found the delivery mechanical or arrogant, and the file layout confusing at the start
This is amazing. I learned so much with this course and I finally after many years of studying ML felt confident with writing RL code.
Daniel Bresnan, a student

Expert level is accurate: it suits people with deep learning, Python and undergraduate math already in hand.

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What you will learn

  • Review Reinforcement Learning Basics: MDPs, Bellman Equation, Q-Learning
  • Theory and Implementation of Deep Q-Learning / DQN
  • Theory and Implementation of Policy Gradient Methods and A2C (Advantage Actor-Critic)
  • Apply DQN and A2C to Atari Environments (Breakout, Pong, Asteroids, etc.)
  • VIP Only: Apply A2C to Build a Trading Algorithm for Multi-Period Portfolio Optimization

Course content

13 sections · 66 lectures · 16 hours of video

  1. 1Welcome 3 free previews5 lectures · 18 min
  2. 2Preliminaries (Concepts)5 lectures · 1.9 hours
  3. 3Preliminaries (Coding)4 lectures · 1.4 hours
  4. 4DQN / Deep Q-Learning7 lectures · 1.7 hours
  5. 5Policy Gradient Methods and A2C11 lectures · 3.7 hours
  6. 6Atari Environments6 lectures · 2.1 hours
  7. 7Multi-Period Portfolio Optimization (Preview Only!) 1 free preview2 lectures · 7 min
  8. 8Background Review for Reinforcement Learning12 lectures · 2 hours
  9. 9Appendix / FAQ1 lecture · 4 min
  10. 10Setting Up Your Environment (FAQ)3 lectures · 42 min

Who it is for

The instructor says it suits

  • Machine Learning & AI enthusiasts who want to explore one of the most exciting fields in AI: reinforcement learning
  • Software developers and engineers looking to build intelligent agents that learn from experience
  • Quantitative finance professionals interested in applying RL to portfolio optimization and algorithmic trading
  • Students and researchers studying AI, computer science, or data science who want hands-on experience with real RL implementations
  • Game developers curious about using RL to train AI for complex behaviors and adaptive gameplay
  • Robotics practitioners who want to learn how agents can make sequential decisions in physical environments
  • Data scientists aiming to expand their toolkit beyond supervised learning / unsupervised learning
  • Traders and investors looking to apply cutting-edge AI methods to automated trading strategies

What you need before you start

  • Reinforcement Learning fundamentals: MDPs, Bellman Equation, Monte Carlo Methods, Temporal Difference Learning
  • Undergraduate STEM math: calculus, probability, statistics
  • Python programming and numerical computing (Numpy, Matplotlib, etc.)
  • Deep Learning fundamentals: Convolutional neural networks, hyperparameter optimization, etc.

How it compares

Against the other 461 rated Udemy courses in Data Science on CourseDuck:

  • Rating4.7 out of 5higher than 85% of them
  • Reviews516#232 of 462 by number of reviews
  • Length15.7 hourslonger than 71% of them
  • Last updatedFebruary 2026more recently than 68% of them

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Course Description

Are you ready to unlock the power of Reinforcement Learning (RL) and build intelligent agents that can learn and adapt on their own?

Welcome to the most comprehensive, up-to-date, and practical course on Reinforcement Learning, now in its highly improved Version 2! Whether you're a student, researcher, engineer, or AI enthusiast, this course will guide you from foundational RL concepts to advanced Deep RL implementations — including building agents that can play Atari games using cutting-edge algorithms like DQN and A2C.

What You’ll Learn

  • Core RL Concepts: Understand rewards, value functions, the Bellman equation, and Markov Decision Processes (MDPs).

  • Classical Algorithms: Master Q-Learning, TD Learning, and Monte Carlo methods.

  • Hands-On Coding: Implement RL algorithms from scratch using Python and Gymnasium.

  • Deep Q-Networks (DQN): Learn how to build scalable, powerful agents using neural networks, experience replay, and target networks.

  • Policy Gradient & A2C: Dive into advanced policy optimization techniques and learn how actor-critic methods work in practice.

  • Atari Game AI: Use modern libraries like Stable Baselines 3 to train agents that play classic Atari games — from scratch!

  • Bonus Concepts: Explore evolutionary methods, entropy regularization, and performance tuning tips for real-world applications.

Tools and Libraries

  • Python (with full code walkthroughs)

  • Gymnasium (formerly OpenAI Gym)

  • Stable Baselines 3

  • NumPy, Matplotlib, PyTorch (where applicable)

Why This Course?

  • Version 2 updates: Streamlined content, clearer explanations, and updated libraries.

  • Real implementations: Go beyond theory by building working agents — no black boxes.

  • For all levels: Includes a dedicated review section for beginners and deep dives for advanced learners.

  • Proven structure: Designed by an experienced instructor who has taught thousands of students to success in AI and machine learning.

Who Should Take This Course?

  • Data Scientists and ML Engineers who want to break into Reinforcement Learning

  • Students and Researchers looking to apply RL in academic or practical projects

  • Developers who want to build intelligent agents or AI-powered games

  • Anyone fascinated by how machines can learn through interaction

Join thousands of learners and start mastering Reinforcement Learning today — from theory to full implementations of agents that think, learn, and play.

Enroll now and take your AI skills to the next level!
Read More Expert Expert Level product length iconTime: 15.7h

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Instructor Details

Lazy Programmer Inc.

The Lazy Programmer is a seasoned online educator with an unwavering passion for sharing knowledge. With over 10 years of experience, he has revolutionized the field of data science and machine learning by captivating audiences worldwide through his comprehensive courses and tutorials.


Equipped with a multidisciplinary background, the Lazy Programmer holds a remarkable duo of master's degrees. His first foray into academia led him to pursue computer engineering, with a specialized focus on machine learning and pattern recognition. Undeterred by boundaries, he then ventured into the realm of statistics, exploring its applications in financial engineering.


Recognized as a trailblazer in his field, the Lazy Programmer quickly embraced the power of deep learning when it was still in its infancy. As one of the pioneers, he fearlessly embarked on instructing one of the first-ever online courses on deep learning, catapulting him to the forefront of the industry.


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Reviews

4.7

516 ratings on Udemy

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By Jennifer Noble on 5/27/2026

Takes a very "hand-held" approach to teaching even basic things like the replay buffer, which is great for people that might be a little intimidated by this.

By Eric Le Saux on 4/8/2026

Really good course for me. Challenging!

By Robert Golding on 2/16/2026

Really enjoying this course so far. It's up to date and interesting.

By Liam Millard on 11/3/2025

It's really informative and easy to follow

By Daniel Bresnan on 11/3/2025

This is amazing. I learned so much with this course and I finally after many years of studying ML felt confident with writing RL code. Thank you Lazy Programmer. :)

By Michael Schiller on 10/16/2025

Very precise in the theory. You can skip a lot of the lessons, if you are familiar with computer science/machine learning from your bachelor or master degree.

By Rodrigo Macedo on 10/12/2025

Excellent course and instructor. Lazy Programmer's explanations are very detailed and clear. And maybe one of the most important aspects is that Lazy Programmer or his team quickly replies to every single question you may have.

By Nisarga Nilavadi on 10/2/2025

The explanations are very mechanical. I couldn't get past a 2 chapters.

By Michel Karam on 8/18/2025

Everything that I've needed to know about Deep Reinforcement Learning I've been able to go back and access it in one place. The videos are made in a simple way that makes it easy to learn the concepts.

By Gabriel Cassini on 6/4/2025

The course is fantastic, and covers both syntax and theory for deep reinforcement learning. Beware that this is an advanced course. There are advanced concepts from deep learning, probability, and reinforcement learning, and you will need to cover about 2 years of material before reaching this level. One of the best things is that the course is clean and organized. Each section is devoted to a different topic, and the code is likewise clean and easy to follow, and each notebook follows the same template. It gives you a chance to absorb what you have learned through repetition, and at the end you get to apply it to a portfolio trading project. Highly recommend this course!

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Overall Score : 94 / 100

Advanced AI: Deep Reinforcement Learning in PyTorch (v2)
Visit course on Udemy Write a review
  • Video Duration: icon 15.7h
  • Price: $19.99
  • Provider: icon Udemy
  • Updated: icon February 2026
  • Level: Expert Expert
  • Language: icon English
  • Certificate: icon Yes
  • Lectures:66 in 13 sections
  • Students:3,909
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