Udemy is an online learning platform that offers multiple Reinforcement Learning courses. Moreover, Udemy Reinforcement Learning courses cover various topics such as Policy Evaluation, Iteration, Exploration, Policy Gradients, etc.
So, if you are a beginner or a professional candidate looking to improve your skills, Reinforcement Learning courses on Udemy can meet your needs. Also, these courses are affordable, have lifetime access, and have the convenience of self-paced learning.
Some of the top rated Reinforcement Learning Courses on Udemy in 2024 are listed below for your reference along with their learning outcomes, ratings and duration.
| Best Deep Learning Courses on Udemy | Best AI Courses on Udemy |
1. Future Artificial Intelligence Reinforcement Learning (TM)
“Future Artificial Intelligence Reinforcement Learning (TM)” teaches the recent developments in reinforcement learning. It also emphasizes the possible effects on the next generation of technologies. In this course, students can learn Deep reinforcement learning, model-based and model-free approaches, exploration-exploitation trade-offs, and other subjects. This course is perfect for professionals, researchers, and AI enthusiasts who want to master the newest AI developments.
- Course Rating: 4.9/5
- Duration: 1 Hour 2 Minutes
- Benefits: Access on mobile and TV, Full lifetime access, Certificate of completion
Learning Outcomes
| Industry Automation with Reinforcement Learning | Applications in Trading and Finance |
| Reinforcement Learning Applications in Healthcare | More Research In Reinforcement Learning Applications |
2. PyTorch: Deep Learning and Artificial Intelligence
“PyTorch: Deep Learning and Artificial Intelligence” is for students who want to pick up information quickly. However, there are also “in-depth” explanations if you want to learn about the theory (such as what a loss function is and what kinds of gradient descent techniques there are) with PyTorch.
- Course Rating: 4.8/5
- Duration: 24 Hours 23 Minutes
- Benefits: Access on mobile and TV, Full lifetime access, Certificate of completion
Learning Outcomes
| Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs) | Natural Language Processing (NLP) with Deep Learning |
| Time Series Forecasting | Predict Stock Returns |
3. Reinforcement Learning: AI Flight with Unity ML-Agents
“Reinforcement Learning: AI Flight with Unity ML-Agents” teaches you how neural networks evolve in a real-time 3D environment. This teaching is based on incentives for good behavior and reinforcement Learning with ML agents. Also, this course puts lessons to your video game concepts making it more enjoyable.
- Course Rating: 4.8/5
- Duration: 9 Hours 56 Minutes
- Benefits: 5 articles, 4 downloadable resources, Access on mobile and TV, Full lifetime access, Certificate of completion
Learning Outcomes
| To install, run, and train neural networks with Unity ML-Agents | Train airplane agents to fly with Reinforcement Learning, specifically PPO |
| Utilize Machine Learning at a high level | Lots of opportunities to customize the project and make it your own |
4. Curiosity-Driven Deep Reinforcement Learning
In this advanced course on deep reinforcement learning, students will learn how to implement cutting-edge artificial intelligence research papers from scratch. This is a fast-paced course for those experienced in coding up actor-critic agents on their own.
- Course Rating: 4.8/5
- Duration: 3 Hours 45 Minutes
- Benefits: Access on mobile and TV, Certificate of completion
Learning Outcomes
| To code a3c agents | To do parallel processing in Python |
| To implement deep reinforcement learning papers | To code the intrinsic curiosity module |
5. Artificial Intelligence: Reinforcement Learning in Python
“Artificial Intelligence: Reinforcement Learning in Python” provides new and astonishing insights about both behavioral psychology and neuroscience. This course is the closest approach to real artificial general intelligence.
- Course Rating: 4.7/5
- Duration: 14 Hours 47 Minutes
- Benefits: Access on mobile and TV, Full lifetime access, Certificate of completion
Learning Outcomes
| Application of gradient-based supervised machine learning methods | Understanding reinforcement learning on a technical level |
| Understanding the relationship between reinforcement learning and psychology | Implementing 17 different reinforcement learning algorithms |
6. Advanced AI: Deep Reinforcement Learning in Python
In this course, students will examine the RBF network, a special kind of neural network, the policy gradient method, the TD Lambda algorithm, and Deep Q-Learning (DQN) and A3C (Asynchronous Advantage Actor-Critic). The course aims to teach modern AI approaches to professionals and students with strong technical backgrounds.
- Course Rating: 4.7/5
- Duration: 10 Hours 43 Minutes
- Benefits: Access on mobile and TV, Full lifetime access, Certificate of completion
Learning Outcomes
| Build various deep-learning agents | Reinforcement Learning with RBF Networks |
| Q-Learning with Deep Neural Networks | Apply a variety of advanced reinforcement learning algorithms to any problem |
| Policy Gradient Methods with Neural Networks | Usage of Convolutional Neural Networks with Deep Q-Learning |
7. Machine Learning Applied to Stock & Crypto Trading – Python
“Machine Learning Applied to Stock & Crypto Trading – Python” is a practical course with theoretical instruction. Students can quickly learn the fundamental ideas and they can also understand the application and utilize it right away. The course is designed for enthusiasts seeking a useful and enjoyable use of machine learning.
- Course Rating: 4.7/5
- Duration: 17 Hours 49 Minutes
- Benefits: 2 articles, 4 downloadable resources, Access on mobile and TV, Full lifetime access, Certificate of completion
Learning Outcomes
| Understand hidden states and regimes for any market or asset using Hidden Markov Models | Test for market efficiency on any given asset |
| Make objective future predictions on financial data with XGBOOST | Train an AI Reinforcement Learning agent to trade stocks with PPO |
| Become familiar with Python Libraries including Pandas, PyTorch (for deep learning), and sklearn | – |
8. Artificial Intelligence IV – Reinforcement Learning in Java
“Artificial Intelligence IV – Reinforcement Learning in Java” teaches about reinforcement learning. This course discusses about underlying mathematics: a Markov Decision Process may be used as a framework for reinforcement learning. Also, three approaches may be used to overcome the issue: value iteration, policy iteration, and Q-learning.
- Course Rating: 4.7/5
- Duration: 3 Hours 2 Minutes
- Benefits: 6 articles, 1 downloadable resource, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Understand reinforcement learning | Understand Markov’s Decision Processes |
| Understand value- and policy-iteration | Understand the Q-learning approach and its applications |
9. Tensorflow 2.0: Deep Learning and Artificial Intelligence
“Tensorflow 2.0: Deep Learning and Artificial Intelligence” has “in-depth” lessons for professionals, but students who want to learn quickly can also learn. In addition to brand-new applications, such as time series forecasting and market forecasts, you will learn how to update your old code to utilize Tensorflow 2.0.
- Course Rating: 4.6/5
- Duration: 23 Hours 35 Minutes
- Benefits: Access on mobile and TV, Full lifetime access, Certificate of completion
Learning Outcomes
| Computer Vision | Use TensorFlow Distribution Strategies to parallelize learning |
| Use Tensorflow Serving to serve your model using a RESTful API | Natural Language Processing (NLP) with Deep Learning |
| Transfer Learning to create state-of-the-art image classifiers | Time Series Forecasting |
10. Cutting-Edge AI: Deep Reinforcement Learning in Python
“Cutting-Edge AI: Deep Reinforcement Learning in Python” will demonstrate multiple approaches, such as the potent A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolutionary tactics. It also teaches about evolution strategies that eliminate all previous theories in favor of a more “black box” strategy.
- Course Rating: 4.6/5
- Duration: 8 Hours 36 Minutes
- Benefits: Access on mobile and TV, Certificate of completion
Learning Outcomes
| Understanding a cutting-edge implementation of the A2C algorithm | Understanding and implementing Evolution Strategies (ES) for AI |
| Understanding and implementing DDPG | – |
11. Reinforcement Learning beginner to master – AI in Python
“Reinforcement Learning beginner to master – AI in Python” is an in-depth exploration of Reinforcement Learning, a fundamental component of modern artificial intelligence. It covers the basics, adaptive algorithms, and their practical implementation for solving control tasks based on experience.
- Course Rating: 4.4/5
- Duration: 10.5 hours
- Benefits: Assignments, 21 articles, 1 downloadable resource, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Understand the Reinforcement Learning paradigm and the tasks that it’s best suited to solve. | Understand the process of solving a cognitive task using Reinforcement Learning |
| Understand the different approaches to solving a task using Reinforcement Learning and choose the most fitting | Implement Reinforcement Learning algorithms completely from scratch |
12. Practical AI with Python and Reinforcement Learning
“Practical AI with Python and Reinforcement Learning” is ideal for understanding and using Artificial Intelligence with Python. It is focused on Neural Networks and Reinforcement Learning to create intelligent agents. Also, it covers multiple topics, including Artificial Neural Networks, Convolutional Neural Networks, Q-Learning, Deep Q-Learning, SARSA, Cross Entropy Methods, Double DQN, and more.
- Course Rating: 4.5/5
- Duration: 26.5 hours
- Benefits: Assignments, 6 articles, 9 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Using TensorFlow to Create Convolution Neural Networks for Images | Using OpenAI to work with built-in game environments |
| Reinforcement Learning with Python | Creating Artificial Neural Networks with TensorFlow |
13. Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)
This comprehensive deep reinforcement learning course offers a structured framework for understanding and implementing deep reinforcement learning research papers. These algorithms are applied to solve challenges in the Open AI gym’s Atari library, including games like Pong, Breakout, and Bankheist. The course includes introductory material on reinforcement learning, deep learning using PyTorch, and a practical application to solve the Cart Pole problem from the Open AI gym.
- Course Rating: 4.6/5
- Duration: 7 hours
- Benefits: 25 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| How to read and implement deep reinforcement learning papers | How to code Deep Q learning agents |
| How to Code Double Deep Q Learning Agents | How to code Dueling Deep Q and Dueling Double Deep Q Learning Agents |
| How to write modular and extensible deep reinforcement learning software | How to automate hyperparameter tuning with command line arguments |
14. Deep Reinforcement Learning 2.0 + AI & ChatGPT Bonus
“Deep Reinforcement Learning 2.0 + AI & ChatGPT Bonus” is a deep dive into the Twin-Delayed DDPG AI model, a powerful combination of state-of-the-art AI techniques, including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. This course is designed for data scientists, AI experts, engineers, business professionals, tech students, or anyone with a passion for artificial intelligence, looking to elevate their skills and tackle advanced AI challenges.
- Course Rating: 4.5/5
- Duration: 9.5 hours
- Benefits: 8 articles, 1 downloadable resource, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Q-Learning | Deep Q-Learning |
| Policy Gradient | Actor-Critic |
| The Foundation Techniques of Deep Reinforcement Learning | How to implement a state-of-the-art AI model that is overperforming the most challenging virtual applications |
15. Advanced Reinforcement Learning: Policy Gradient Methods
“Advanced Reinforcement Learning: Policy Gradient Methods” course series teaches Reinforcement Learning, focusing on implementing powerful Deep Reinforcement Learning algorithms using PyTorch and PyTorch Lightning. This course is ideal for developers seeking jobs in Machine Learning, data scientists, and other professionals.
- Course Rating: 4.8/5
- Duration: 7.5 hours
- Benefits: 10 articles, 1 downloadable resource, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Master some of the most advanced Reinforcement Learning algorithms. | Learn how to create AIs that can act in a complex environment to achieve their goals. |
| Create from scratch advanced Reinforcement Learning agents using Python’s most popular tools (PyTorch Lightning, OpenAI gym, Optuna) | Learn how to perform hyperparameter tuning (Choosing the best experimental conditions for our AI to learn) |
| Fundamentally understand the learning process for each algorithm. | – |
16. Advanced Reinforcement Learning in Python: cutting-edge DQNs
This advanced Udemy course is a comprehensive exploration of Reinforcement Learning. It focuses on the implementation of potent Deep Reinforcement Learning algorithms using PyTorch and PyTorch Lightning. Also, it caters to developers aiming for Machine Learning careers, data scientists, ML practitioners, robotics students, engineering students, and researchers looking to expand their expertise in Reinforcement Learning.
- Course Rating: 4.5/5
- Duration: 8.5 hours
- Benefits: Assignments, 14 articles, 1 downloadable resource, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Master some of the most advanced Reinforcement Learning algorithms. | Learn how to create AIs that can act in a complex environment to achieve their goals. |
| Create from scratch advanced Reinforcement Learning agents using Python’s most popular tools (PyTorch Lightning, OpenAI gym, Optuna) | Learn how to perform hyperparameter tuning (Choosing the best experimental conditions for our AI to learn) |
| Fundamentally understand the learning process for each algorithm. | – |
17. Modern Reinforcement Learning: Actor-Critic Agents
This advanced deep reinforcement learning course teaches the implementation of various algorithms, including policy gradient, actor-critic, DDPG, TD3, and SAC. Moreover, this course is designed for highly motivated and advanced learners with a strong background in calculus, reinforcement learning, and deep learning, providing the skills to translate research papers into functional code efficiently.
- Course Rating: 4.5/5
- Duration: 10.5 hours
- Benefits: 58 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| How to code policy gradient methods in PyTorch | How to code Deep Deterministic Policy Gradients (DDPG) in PyTorch |
| How to code Twin Delayed Deep Deterministic Policy Gradients (TD3) in PyTorch | How to code actor-critic algorithms in PyTorch |
| How to implement cutting-edge artificial intelligence research papers in Python | – |
18. Complete Machine Learning & Reinforcement Learning 2024
“Complete Machine Learning & Reinforcement Learning 2024” is designed for beginners and anyone looking to kickstart a career in Machine Learning and Data Science. It covers the fundamentals of machine learning, mathematics, and practical coding in Python from scratch. It’s suitable for beginners in Machine Learning and those seeking to enhance their data science and mathematical skills.
- Course Rating: 4.5/5
- Duration: 27 hours
- Benefits: Assignments, 12 articles, 25 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Achieve mastery in machine learning from simple linear regression to advanced reinforcement learning projects. | Get a deeper intuition about different Machine Learning nomenclatures. |
| Be able to manipulate different algorithms with the power of Mathematics. | Write different kinds of algorithms from scratch with Python. |
19. Deep Reinforcement Learning using Python
This course is a comprehensive introduction to Deep Reinforcement Learning, a sub-field of machine learning. It covers the fundamentals of deep reinforcement learning, including policies, value functions, Q functions, and neural networks. The course guides students through setting up their virtual environments and installing the necessary packages.
- Course Rating: 4.2/5
- Duration: 5 hours
- Benefits: Assignments, 9 articles, 15 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Understand deep reinforcement learning and its applications | Build your neural network |
| Implement 5 different reinforcement learning projects | Learn a lot of ways to improve your robot |
20. Machine Learning: Beginner Reinforcement Learning in Python
“Machine Learning: Beginner Reinforcement Learning in Python” is a beginner-friendly course that teaches the concept of reinforcement learning. It also teaches participants to code a neural network in Python for delayed gratification. The course uses the NChain game from the Open AI Institute, where the computer earns rewards by making strategic decisions. In this course, participants will also learn about Deep Q Learning, used for teaching neural networks to excel in games like chess, Go, and Atari.
- Course Rating: 4.2/5
- Duration: 5 hours
- Benefits: Assignments, 9 articles, 15 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Machine Learning | Artificial Intelligence |
| Neural Networks | Reinforcement Learning |
| Deep Q Learning | OpenAI Gym |
Ans. The “Future Artificial Intelligence Reinforcement Learning (TM)” course is the highest-rated and most popular course on Udemy with 4.9 stars out of 5. It has the following benefits: 1-hour on-demand video, Access on mobile and TV, Full lifetime access, Certificate of completion
Ques. Are Udemy Reinforcement learning courses worth it?
Ans. Yes, there are 10,000 courses to choose from on Udemy based on Reinforcement learning. Most of the courses have the highest paid subscribers and have a rating above 4.6 out of 5.
Ques. Are there any courses on Reinforcement learning that are suitable for beginners?
Ans. Given below are some of the highest-rated Reinforcement learning courses for beginners on Udemy:
- Machine Learning Applied to Stock & Crypto Trading – Python
- Machine Learning: Beginner Reinforcement Learning in Python
- Artificial Intelligence for Simple Games
- Deep Reinforcement Learning 2.0
Ques. What is the top online Reinforcement learning course available through Udemy?
Ans. “Tensorflow 2.0: Deep Learning and Artificial Intelligence” is the best Reinforcement learning course on Udemy with a 4.6 rating out of 5 and a total of 45,000+ student enrolment.
Ques. How can I get free courses on Udemy?
Ans. On Udemy, there are many free courses available for each subject. Google may be used to look up Udemy Free Courses.
Ques. What are the requirements for courses in Reinforcement learning?
Ans. The basic requirements for each Reinforcement learning course are different. However, basic knowledge of programming, Mathematics, and statistics, Familiarity with artificial intelligence, Programming frameworks, and tools are some common prerequisites.
Ques. Can I look at the course materials before registering at Udemy?
Ans. Yes, every course’s details are given in brief including the course description, rating, benefits, tutor details, and price. You can look at any of these by clicking on a course of your choice.
Ques. Are Reinforcement learning courses eligible for certifications?
Ans. Yes, the enrolled students are given a certification of completion along with access to viewing the content on mobile and TV.
Ques. What is Reinforcement Learning?
Ans. Reinforcement Learning (RL) is a type of machine learning paradigm where an agent learns to make sequences of decisions by interacting with an environment.
Ques. What are the topics covered in Reinforcement Learning?
Ans. Reinforcement learning covers a range of topics that are essential to understanding and implementing effective learning strategies for agents interacting with environments. MDPs, Dynamic Programming, Temporal Difference Learning, Deep Reinforcement Learning, and Policy Gradient Methods are some of the topics covered in Reinforcement Learning.
How to code policy gradient methods in PyTorch?
To code policy gradient methods in PyTorch, first create a neural network to represent the policy. Then, define a function to compute the loss based on the actions and rewards. Finally, optimize the model parameters using PyTorch’s optimization functions.
Is it beneficial to learn Natural Language Processing (NLP) with Deep Learning?
Yes, learning NLP with Deep Learning can be beneficial. Deep Learning techniques have revolutionized NLP to create accurate and sophisticated models. They are for tasks like sentiment analysis, language translation, and text generation. Hence, understanding NLP with Deep Learning opens up opportunities in various fields where it is essential to analyze and process large volumes of text data.
Is it tough to learn Markov’s Decision Processes?
Learning Markov Decision Processes (MDPs) can be challenging. MDPs involve understanding how agents make decisions in stochastic environments based on states, actions, rewards, and transition probabilities. So, the mathematical framework behind MDPs can seem complex. However, breaking down the concepts and starting with simpler examples can help beginners.
Can a beginner learn to code a3c agents?
Yes, a beginner can learn to code A3C (Asynchronous Advantage Actor-Critic) agents. A3C is a complex reinforcement learning algorithm, so there are beginner-friendly tutorials and courses. Regardless, you can start with simpler reinforcement learning concepts to build up to A3C.
What are some of the recent developments of recent developments in reinforcement learning?
Recent developments in reinforcement learning include advancements in deep reinforcement learning algorithms (such as improvements in model-based approaches, exploration strategies, and policy optimization techniques). Also, there is a growing interest in applying it in robotics, autonomous vehicles, and healthcare.