Top 10 Free Resources To Learn Reinforcement Learning
Reinforcement learning is one of the most popular machine learning techniques among organisations to develop solutions like recommendation systems, healthcare, robotics, transportations, among others. This learning technique follows the “trial and error” method and interacts with the environment to learn an optimal policy for gaining maximum rewards by making the right decisions. In this article, we list down the top 10 free resources to learn reinforcement learning. (The list is in no particular order) 1| Reinforcement Learning Explained Source: edX About: In this course, you will understand the basics of reinforcement learning. You will also learn reinforcement learning problems and other classic examples like news recommendation, navigating in a grid-world, among others. You will also explore the basic algorithms such as dynamic programming, temporal difference learning, progress towards larger state space using function approximation, etc. Click here to learn. 2| Reinforcement Learning Source: Udacity About: Through a combination of classic papers and more recent work, in this course, 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. Click here to learn. 3| Advanced Deep Learning & Reinforcement Learning Source: Youtube About: This course, taught originally at UCL has two parts that are machine learning with deep neural networks and prediction and control using reinforcement learning. The deep learning stream of the course includes an introduction to neural networks and supervised learning with TensorFlow. It also includes lectures on convolutional neural networks, recurrent neural networks, optimisation methods. The reinforcement learning stream includes topics like Markov decision processes, planning by dynamic programming, value function approximation, policy gradient methods, integration of learning and planning, among others. Click here to learn. 4| Deep Reinforcement Learning Source: UC Berkeley Blog About: In this course, you will learn a more advanced part than just the basic introduction to reinforcement learning. For understanding this course, you will need to have some familiarity with reinforcement learning, numerical optimisation, and ML. The course includes topics such as imitation learning, policy gradients, model-based reinforcement learning and other such. Click here to learn. 5| An Introduction to Reinforcement Learning Source: Blog About: In this e-book, you will learn a basic introduction to reinforcement learning, its elements, limitations and scopes. You will also learn some of the important topics such as Monte Carlo methods, finite Morkov decision processes, temporal-difference learning, policy gradient methods, and much more. Click here to learn. 6| An Introduction to Reinforcement Learning Source: freeCodeCamp About: In this tutorial, you will learn the different architectures used to solve reinforcement learning problems, which include Q-learning, Deep Q-learning, Policy Gradients, Actor-Critic, and PPO. You will also learn the basics of reinforcement learning and how rewards are the central idea of reinforcement learning and other such. Click here to learn. 7| Deep Reinforcement Learning and Control Source: GitHub Blog About: In this tutorial, you will learn to implement and experiment with existing algorithms for learning control policies guided by reinforcement, expert demonstrations or self-trials, evaluate the sample complexity, generalisation and generality of these algorithms. After the completion of this tutorial, you will be able to comprehend research papers in the field of robotics learning. Click here to learn. 8| Reinforcement Learning Specialisation Source: Coursera About: In this course, you will learn how reinforcement learning solutions assist in solving real-world problems with the help of trial-and-error interaction as well as how to implement a complete reinforcement learning solution from the beginning to end. After successful completion of this tutorial, you will understand the foundations of modern probabilistic AI and solve real-world problems. Click here to learn. 9| Reinforcement Learning Source: Online NPTEL Courses About: The goal of the course is to introduce the basic mathematical foundations of reinforcement learning, as well as highlight some of the recent directions of research. The course includes an introduction to RL, policy gradient methods, Bellman equations, MDP formulation, dynamic programming, Monte Carlo methods and much more. Click here to learn. 10| Reinforcement Learning Winter 2020 Source: Stanford Education About: This course will provide an introduction to reinforcement learning, and you will learn about the core challenges and approaches of RL that includes generalisation as well as exploration. The course includes a combination of lectures including written and coding assignments. Click here to learn.
Reinforcement learning is one of the most popular machine learning techniques among organisations to develop solutions like recommendation systems, healthcare, robotics, transportations, among others. This learning technique follows the “trial and error” method and interacts with the environment to learn an optimal policy for gaining maximum rewards by making the right decisions. In this article, […]
Reinforcement learning is one of the most popular ML techniques among organisations to develop solutions like healthcare, robotic, etc.
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["Go", "reinforcement learning algorithms", "machine learning", "Reinforcement Learning", "AI", "neural network", "ML", "recommendation systems", "Git", "deep learning", "reinforcement learning models", "policy gradient", "tensorflow gradient", "TensorFlow", "R"]
["AI", "machine learning", "ML", "deep learning", "neural network", "TensorFlow", "recommendation systems", "R", "Go", "Git"]
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.