Free Online Reinforcement Learning Courses and Certifications 2024
Reinforcement Learning is a type of machine learning that enables an agent to learn in an interactive environment by performing actions and receiving rewards or punishments. It is based on the idea of trial and error, where the agent learns from its mistakes and gradually improves its performance.
Popular Courses
This course, State-of-the-art Research of Deep Reinforcement-learning, is the perfect way to get up to date with the newest state-of-the-art Deep reinforcement-learning research knowledge. Led by Nitsan Soffair, a Deep RL researcher at BGU, this course will provide you with the latest research from OpenAI, DeepMind, Google, and Microsoft. You will learn advanced exploration methods, chatbot based Deep RL, evaluation strategies, advanced RL metrics, navigating robot get human language instructions, and more. Validate your knowledge by answering short quizzes of each lecture and complete the course in ~2 hours. Don't miss out on this opportunity to stay ahead of the curve!
Learn More This course introduces you to the world of Reinforcement Learning (RL) and its application to trading strategies. You will learn how to integrate RL with neural networks and apply LSTMs to time series data. By the end of the course, you will be able to build trading strategies using RL, differentiate between actor-based policies and value-based policies, and incorporate RL into a momentum trading strategy. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning. Experience with SQL and a background in statistics and financial markets is recommended. Click now to learn how to use RL to build trading strategies!
Learn More This DeepMind x UCL RL Lecture Series is a great opportunity to learn about the fundamentals of Reinforcement Learning. Research Scientist Hado van Hasselt will introduce the course and explain how it relates to Artificial Intelligence. The lecture series includes slides and full video lectures, making it an ideal way to gain a comprehensive understanding of the subject. Don't miss out on this chance to learn about the exciting field of Reinforcement Learning.
Learn More This course is perfect for anyone wanting to get started with Reinforcement Learning. In just 3 hours, you'll learn the fundamentals of RL with Python, OpenAI Gym and Stable Baselines. You'll be able to build deep learning powered agents to solve a variety of RL problems, and even create your own environment. With this course, you'll learn how to setup Stable Baselines, understand OpenAI Gym environments, train a Reinforcement Learning model, evaluate and test agents, and even build custom OpenAI Gym environments. Get started with Reinforcement Learning today!
Learn More This 9-hour course is the perfect way to learn how to apply machine learning to gaming. It covers reinforcement learning tutorials for gaming using Python and Stable Baselines 3. You'll learn best practices for training reinforcement learning models for games, as well as how to preprocess environments, build RL models, run them live, and more. Plus, you'll get to practice on Mario, Doom, and Streetfighter. Connect with the instructor, Nick, on LinkedIn, Facebook, GitHub, and Patreon for support and discussion. Get ready to take your gaming to the next level with this comprehensive course!
Learn More Nitsan Soffair, a Deep RL researcher at BGU, is offering a course on the newest state-of-the-art Deep reinforcement-learning knowledge. In this course, you will learn about model types, algorithms and approaches, function approximation, deep reinforcement-learning, and deep multi-agent reinforcement-learning. You will also be able to validate your knowledge by answering short and very short quizzes of each lecture. The course can be completed in approximately two hours. Don't miss out on this opportunity to learn the latest in Deep RL!
Learn More This CS25 I Stanford Seminar is a great opportunity to learn about the revolutionary technology of transformers. Led by Andrej Karpathy, the course will explore the details of how transformers work, and dive deep into the different kinds of transformers and how they're applied in different fields. From computer vision to reinforcement learning, GANs, speech, and even biology, transformers have enabled the creation of powerful language models like GPT-3 and were instrumental in DeepMind's AlphaFold2. Don't miss this chance to learn from the experts and explore the future of transformers. Click now to join the course and go forth and transform!
Learn More This MIT 6S191: Reinforcement Learning course provides an introduction to deep learning and its applications. Lecturer Alexander Amini will cover topics such as classes of learning problems, definitions, the Q function, deep Q networks, Atari results and limitations, policy learning algorithms, discrete vs continuous actions, training policy gradients, RL in real life, VISTA simulator, AlphaGo and AlphaZero and MuZero, and a summary. With all lectures, slides, and lab materials available online, this course is perfect for anyone interested in learning more about deep learning and its applications. Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!
Learn More This Deep Reinforcement Learning 2.0 course is the perfect opportunity to learn and implement a new incredibly smart AI model. With this course, you will learn and understand the fundamentals of Artificial Intelligence, including Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more. You will also gain an in-depth understanding of the Twin-Delayed DDPG model and its training process. Finally, you will be able to implement the model from scratch, step by step, and practice coding exercises on the free and open source AI platform, Google Colab. Don't miss out on this amazing opportunity to master this highly advanced model!
Learn More This Reinforcement Learning course on Udemy is the most comprehensive one available. It covers the three paradigms of modern artificial intelligence, and teaches you how to implement adaptive algorithms from scratch. You will learn to combine these algorithms with Deep Learning techniques and neural networks, giving rise to the branch known as Deep Reinforcement Learning. This course will give you the foundation you need to understand new algorithms as they emerge, and prepare you for more advanced courses. It is focused on developing practical skills, and you will implement algorithms in jupyter notebooks from scratch. Don't miss this opportunity to master Reinforcement Learning and AI in Python!
Learn More This advanced course on deep reinforcement learning will teach you how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and soft actor critic (SAC) algorithms in a variety of challenging environments. With a strong focus on dealing with environments with continuous action spaces, this course is perfect for those looking to do research into robotic control with deep reinforcement learning. You will learn a repeatable framework for quickly implementing the algorithms in advanced research papers, and master the answers to the fundamental questions in Actor-Critic methods. If you are a highly motivated and advanced student with prior course work in calculus, reinforcement learning, and deep learning, this course is for you.
Learn More This course is perfect for anyone interested in the intersection of video games and artificial intelligence. With Unity ML-Agents, you can watch your neural network learn in a real-time 3d environment based on rewards for good behavior. Learn how to use and train the example content, create custom assets with Blender, and build a full game with menus for level and difficulty selection. No prior knowledge of deep learning or reinforcement learning is required. By the end of the course, you'll have a complete game that you can share, add to your portfolio, or sell.
Learn More This course is perfect for those interested in Artificial Intelligence and Reinforcement Learning. It covers the mathematical background of Reinforcement Learning, such as Markov Decision Processes, value-iteration, policy-iteration and Q-learning. It also covers pathfinding algorithms with Q-learning and Q-learning with neural networks. This course is a great way to learn the state-of-the-art approach to Reinforcement Learning and gain a better understanding of Artificial Intelligence.
Learn More This course is perfect for beginners to machine learning. In this course, you will learn to code a neural network in Python capable of delayed gratification. You will be introduced to the concept of reinforcement learning, and use the NChain game provided by the Open AI institute to understand how the computer can get a small reward if it goes backwards, but a much larger reward if it learns to make short term sacrifices by persistently pressing forwards. You will also learn Deep Q Learning - a revolutionary technique invented by Google DeepMind to teach neural networks to play chess, Go and Atari. Join this course to explore the exciting advances in artificial intelligence and learn to code a neural network in Python.
Learn More This Advanced Reinforcement Learning course on Udemy is the most comprehensive of its kind. It will teach you to implement powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will learn to create adaptive Artificial Intelligence agents that can solve decision-making tasks, combining Reinforcement Learning techniques with Neural Networks and Deep Learning methods. This course will also prepare you for the next courses in the series, where you will explore other advanced methods. With practical modules and jupyter notebooks, you will be able to develop your skills from scratch. Don't miss this opportunity to become an expert in Reinforcement Learning!
Learn More This Advanced Reinforcement Learning course on Udemy is the most comprehensive of its kind. It will teach you to implement powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will learn to create adaptive Artificial Intelligence agents that can solve decision-making tasks, combining Reinforcement Learning techniques with Neural Networks and Deep Learning methods. This course will also prepare you for the next courses in the series, where you will explore other advanced methods. With practical skills in mind, you will implement algorithms from scratch in jupyter notebooks. Don't miss out on this opportunity to learn the state of the art in Reinforcement Learning!
Learn More Frequently Asked Questions and Answers
Q1: What Reinforcement Learning courses can I find on AZ Class?
On this page, we have collected free or certified 56 Reinforcement Learning online courses from various platforms. The list currently only displays up to 50 items. If you have other needs, please contact us.
Q2: Can I learn Reinforcement Learning for free?
Yes, If you don’t know Reinforcement Learning, we recommend that you try free online courses, some of which offer certification (please refer to the latest list on the webpage as the standard). Wish you a good online learning experience!