Udemy is an online learning platform that offers a wide range of Reinforcement Learning courses. Udemy’s Reinforcement Learning courses cover various topics such as Policy Evaluation, Iteration, Exploration, Policy Gradients, etc.
Whether the candidate is a complete beginner or an experienced professional looking to improve his/her skills, Reinforcement Learning courses on Udemy cater to their needs, with affordable pricing, lifetime access to course materials, and the convenience of self-paced learning.
Udemy online courses are priced between USD 50 and USD 200, offering flexibility for different budget levels. Students can enroll in any course using the join now links below and get up to 90% discount.
Future Artificial Intelligence Reinforcement Learning (TM)
This course examines the most recent developments in reinforcement learning, with an emphasis on its possible effects on the next generation of technologies. Deep reinforcement learning, model-based and model-free approaches, exploration-exploitation trade-offs, and other subjects will all be covered in depth with the students. 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
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Access on mobile and TV, Full lifetime access, Certificate of completion
Join Now: Future Artificial Intelligence Reinforcement Learning (TM)
Learning Outcomes
| Industry Automation with Reinforcement Learning | Applications in Trading and Finance |
| Reinforcement Learning Applications in Healthcare | More Research In Reinforcement Learning Applications |
PyTorch: Deep Learning and Artificial Intelligence
Professionals and academics from all across the world have chosen PyTorch as their go-to deep learning and AI library. This course is meant for students who want to pick up information quickly, but there are also “in-depth” portions if you want to delve a bit deeper into the theory (such as what a loss function is and what kinds of gradient descent techniques there are). In this course, the emphasis is on breadth rather than depth, with less theory and more emphasis on developing engaging activities.
- Course Rating: 4.8/5
- Duration: 24 Hours 23 Minutes
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Access on mobile and TV, Full lifetime access, Certificate of completion
Join Now: PyTorch: Deep Learning and Artificial Intelligence
Learning Outcomes
| Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs) | Natural Language Processing (NLP) with Deep Learning |
| Time Series Forecasting | Predict Stock Returns |
Reinforcement Learning: AI Flight with Unity ML-Agents
This reinforcement course enables you to witness how neural networks evolve in a real-time 3D environment based on incentives for good behavior, Reinforcement Learning with ML-Agents is inherently easier to use than other machine learning methodologies. Being able to quickly put it to your own video game concepts makes it more enjoyable than dealing with overly simplified example issues in a library like OpenAI Gym.
- Course Rating: 4.8/5
- Duration: 9 Hours 56 Minutes
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: 5 articles, 4 downloadable resources, Access on mobile and TV, Full lifetime access, Certificate of completion
Join Now: Reinforcement Learning: AI Flight with Unity ML-Agents
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 |
Curiosity-Driven Deep Reinforcement Learning
In this advanced course on deep reinforcement learning, motivated students will learn how to implement cutting-edge artificial intelligence research papers from scratch. This is a fast-paced course for those that are experienced in coding up actor-critic agents on their own. you’ll code up two papers in this course, using the popular PyTorch framework.
- Course Rating: 4.8/5
- Duration: 3 Hours 45 Minutes
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Access on mobile and TV, Certificate of completion
Join Now: Curiosity Driven Deep Reinforcement Learning
Learning Outcomes
| To code a3c agents | To do parallel processing in python |
| To implement deep reinforcement learning papers | To code the intrinsic curiosity module |
Artificial Intelligence: Reinforcement Learning in Python
Recently, reinforcement learning has gained popularity for accomplishing major goals and more. New and astonishing insights in both behavioral psychology and neuroscience are provided by this course. As you’ll discover in this course, educating an agent and teaching an animal or even a person are very similar procedures. It is the closest approach to real artificial general intelligence.
- Course Rating: 4.7/5
- Duration: 14 Hours 47 Minutes
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Access on mobile and TV, Full lifetime access, Certificate of completion
Join Now: Artificial Intelligence: Reinforcement Learning in Python
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 |
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) to further their understanding of temporal difference learning. The purpose of this course is to teach modern AI approaches to professionals and students with strong technical backgrounds.
- Course Rating: 4.7/5
- Duration: 10 Hours 43 Minutes
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Access on mobile and TV, Full lifetime access, Certificate of completion
Join Now: Advanced AI: Deep Reinforcement Learning in Python
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 |
Machine Learning Applied to Stock & Crypto Trading – Python
The in-depth theory is not a major component of this course. It is a wholly practical course with high-level theoretical instruction that allows anybody to quickly learn the fundamental ideas but, more significantly, to comprehend the application and utilize this knowledge 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
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: 2 articles, 4 downloadable resources, Access on mobile and TV, Full lifetime access, Certificate of completion
Join Now: Machine Learning Applied to Stock & Crypto Trading – Python
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 | – |
Artificial Intelligence IV – Reinforcement Learning in Java
The topic of this course is reinforcement learning. The first step is to discuss the underlying mathematics: a Markov Decision Process may be used as a framework for reinforcement learning. Three approaches may be used to overcome the issue: value iteration, policy iteration, and Q-learning. Since Q-learning employs a model-free methodology, it is cutting-edge. By interacting with the environment, it discovers the best course of action.
- Course Rating: 4.7/5
- Duration: 3 Hours 2 Minutes
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: 6 articles, 1 downloadable resource, Access on mobile and TV, Certificate of completion
Join Now: Artificial Intelligence IV – Reinforcement Learning in Java
Learning Outcomes
| Understand reinforcement learning | Understand Markov’s Decision Processes |
| Understand value- and policy-iteration | Understand the Q-learning approach and its applications |
Tensorflow 2.0: Deep Learning and Artificial Intelligence
Although there are “in-depth” portions in this course in case you wish to delve a bit further into the theory, it is meant for students who want to learn quickly. In addition to brand-new applications that have never been done before, 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
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Access on mobile and TV, Full lifetime access, Certificate of completion
Join Now: Tensorflow 2.0: Deep Learning and Artificial Intelligence
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 |
Cutting-Edge AI: Deep Reinforcement Learning in Python
This course will demonstrate a variety of approaches, such as the potent A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolutionary tactics. A novel and innovative method of reinforcement learning called evolution strategies essentially eliminates all previous theories in favor of a more “black box” strategy that is motivated by biological evolution.
- Course Rating: 4.6/5
- Duration: 8 Hours 36 Minutes
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Access on mobile and TV, Certificate of completion
Join Now: Cutting-Edge AI: Deep Reinforcement Learning in Python
Learning Outcomes
| Understanding a cutting-edge implementation of the A2C algorithm | Understanding and implementing Evolution Strategies (ES) for AI |
| Understanding and implementing DDPG | – |
Reinforcement Learning beginner to master – AI in Python
This comprehensive Udemy course 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. It’s ideal for developers aspiring to work in Machine Learning, data scientists, analysts, and machine learning practitioners looking to expand their knowledge, as well as researchers seeking to enhance their practical coding skills.
- Course Rating: 4.4/5
- Duration: 10.5 hours
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Assignments, 21 articles, 1 downloadable resource, Full lifetime access, Access on mobile and TV, Certificate of completion
Join Now: Reinforcement Learning beginner to master – AI in Python
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 |
Practical AI with Python and Reinforcement Learning
This online course is your gateway to understanding and using Artificial Intelligence with Python, focusing on Neural Networks and Reinforcement Learning to create intelligent agents. It covers a wide range of 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
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Assignments, 6 articles, 9 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Join Now: Practical AI with Python and Reinforcement Learning
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 |
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 will be 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 in solving the Cart Pole problem from the Open AI gym.
- Course Rating: 4.6/5
- Duration: 7 hours
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: 25 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Join Now: Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)
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 |
Deep Reinforcement Learning 2.0 + AI & ChatGPT Bonus
This course 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
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: 8 articles, 1 downloadable resource, Full lifetime access, Access on mobile and TV, Certificate of completion
Join Now: Deep Reinforcement Learning 2.0 + AI & ChatGPT Bonus
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 |
Advanced Reinforcement Learning: policy gradient methods
This comprehensive Udemy course series offers a deep dive into Reinforcement Learning, focusing on implementing powerful Deep Reinforcement Learning algorithms using PyTorch and PyTorch Lightning. It caters to developers seeking jobs in Machine Learning, data scientists, analysts, machine learning practitioners, robotics students, and engineering students, and researchers eager to expand their knowledge in Reinforcement Learning.
- Course Rating: 4.8/5
- Duration: 7.5 hours
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: 10 articles, 1 downloadable resource, Full lifetime access, Access on mobile and TV, Certificate of completion
Join Now: Advanced Reinforcement Learning: policy gradient methods
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. | – |
Advanced Reinforcement Learning in Python: cutting-edge DQNs
This advanced Udemy course is a comprehensive exploration of Reinforcement Learning, emphasizing the implementation of potent Deep Reinforcement Learning algorithms using PyTorch and PyTorch Lightning. 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
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Assignments, 14 articles, 1 downloadable resource, Full lifetime access, Access on mobile and TV, Certificate of completion
Join Now: Advanced Reinforcement Learning in Python: cutting-edge DQNs
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. | – |
Modern Reinforcement Learning: Actor-Critic Agents
This advanced deep reinforcement learning course offers a deep dive into the implementation of various sophisticated algorithms, including policy gradient, actor-critic, DDPG, TD3, and SAC, in challenging environments from the Open AI gym. 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
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: 58 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Join Now: Modern Reinforcement Learning: Actor-Critic Agents
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 | – |
Complete Machine Learning & Reinforcement Learning 2023
This comprehensive Machine Learning course 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 newcomers to Machine Learning and those seeking to enhance their data science and mathematical skills.
- Course Rating: 4.5/5
- Duration: 27 hours
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Assignments, 12 articles, 25 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Join Now: Complete Machine Learning & Reinforcement learning 2023
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. |
Deep Reinforcement Learning using Python
This course is a comprehensive introduction to Deep Reinforcement Learning, a sub-field of machine learning that combines Reinforcement Learning with deep learning techniques. 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
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Assignments, 9 articles, 15 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Join Now: Deep Reinforcement Learning using python
Learning Outcomes
| Understand deep reinforcement learning and its applications | Build your own neural network |
| Implement 5 different reinforcement learning projects | Learn a lot of ways to improve your robot |
Machine Learning: Beginner Reinforcement Learning in Python
This beginner-friendly course introduces the concept of reinforcement learning and 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. Participants will learn about Deep Q Learning, a groundbreaking technique developed by Google DeepMind for teaching neural networks to excel in games like chess, Go, and Atari. It’s suitable for anyone with an interest in machine learning.
- Course Rating: 4.2/5
- Duration: 5 hours
- Fees: Click on the Join Now link to get a 90% discount
- Benefits: Assignments, 9 articles, 15 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Join Now: Machine Learning: Beginner Reinforcement Learning in Python
Learning Outcomes
| Machine Learning | Artificial Intelligence |
| Neural Networks | Reinforcement Learning |
| Deep Q Learning | OpenAI Gym |
Best Reinforcement Courses on Udemy: FAQs
Ques. Which is the best Reinforcement learning course on Udemy?
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 45,189 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.
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