One of the most intriguing areas of AI today is the concept of deep reinforcement learning
Deep Reinforcement Learning (DRL), a very fast-moving field, is the combination of Reinforcement Learning and Deep Learning. It is also the most trending type of Machine Learning because it can solve a wide range of complex decision-making tasks that were previously out of reach for a machine to solve real-world problems with human-like intelligence. One of the most intriguing areas of artificial intelligence today is the concept of deep reinforcement learning, where machines can teach themselves based on the results of their actions. It is one of the areas of artificial intelligence that shows great promise. Through a series of trial and error, a machine keeps learning, making this technology ideal for dynamic environments that keep changing. Although reinforcement learning has been around for decades, it was much more recently combined with deep learning, which yielded phenomenal results. The “deep” portion of reinforcement learning refers to multiple (deep) layers of artificial neural networks that replicate the structure of a human brain. This article features the top deep reinforcement learning courses to take up in 2022.
Deep Reinforcement Learning
Udacity
Learn cutting-edge deep reinforcement learning algorithms—from Deep Q-Networks (DQN) to Deep Deterministic Policy Gradients (DDPG). Apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects.
Deep Learning and Reinforcement Learning
Coursera
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning and is frequently used to power most of the AI applications that we use daily. First, you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning.
Reinforcement Learning Lecture
Deep Mind
Comprising 13 lectures, the series covers the fundamentals of reinforcement learning and planning in sequential decision problems, before progressing to more advanced topics and modern deep RL algorithms. It gives students a detailed understanding of various topics, including Markov Decision Processes, sample-based learning algorithms (e.g. (double) Q-learning, SARSA), deep reinforcement learning, and more. It also explores more advanced topics like off-policy learning, multi-step updates, and eligibility traces, as well as conceptual and practical considerations in implementing deep reinforcement learning algorithms such as rainbow DQN.
Deep Reinforcement Learning 2.0
Udemy
In this course, you will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state-of-the-art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor-Critic. The model is so strong that for the first time in your courses, you will be able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).
Advanced AI: Deep Reinforcement Learning in Python
Udemy
This course is all about the application of deep learning and neural networks to reinforcement learning. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level.
Reinforcement Learning by Georgia Institute of Technology
Udacity
You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.
Practical AI with Python and Reinforcement Learning
Udemy
In this course, you will create your own deep reinforcement learning agents in your own environments. This course focuses on a practical approach with the right balance of theory and intuition with useable code. You will also learn how Deep Learning with Keras and TensorFlow works, before diving into Reinforcement Learning concepts, such as Q-Learning.
AWS DeepRacer by AWS
Udacity
This course will prepare you to create, train, and fine-tune reinforcement learning models in the AWS DeepRacer 3D racing simulator. You will be able to utilize the car’s tech specs, assembly, and calibration to train and deploy your racing model using AWS in both simulated and real-world tracks.
Tensorflow 2.0: Deep Learning and Artificial Intelligence
Udemy
This course is for beginner-level students. This course starts with some very basic machine learning models and advances to the state of the art concepts. After that, you will learn deep learning concepts, such as Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.
This course includes the following projects:
- Natural Language Processing (NLP)
- Recommender Systems
- Transfer Learning for Computer Vision
- Generative Adversarial Networks (GANs)
- Deep Reinforcement Learning Stock Trading Bot
AWS Machine Learning Foundations Course
Udacity
This is a completely FREE course to learn the fundamentals of advanced machine learning areas such as computer vision, reinforcement learning, and generative AI. You will get hands-on with machine learning using AWS AI Devices (i.e. AWS DeepLens, AWS DeepRacer, and AWS DeepComposer). You will learn how to prepare, build, train, and deploy high-quality machine learning (ML) models quickly with Amazon SageMaker and learn object-oriented programming best practices.
Source: analyticsinsight.net
