Best Reinforcement learning Online Courses
Table of Contents
Welcome to this course: Learn Reinforcement Learning From Scratch. Reinforcement Learning is the next big thing. It is a part of machine learning. Reinforcement learning is one powerful paradigm for making good decisions, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. It allows machines and software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance.
In this course, you’ll learn
- Learn and understand Reinforcement Learning
- Learn how to manage and install software for machine
- Learn how to implement common RL algorithms
- Learn to Generate a Random MDP Problem
- Learn how to solve various reinforcement learning problems
At the end of this course, you will have a logical understanding of Reinforcement learning and know the most appropriate solutions for your problems.
Reinforcement learning (RL) is hot! This branch of machine learning powers AlphaGo and Deepmind’s Atari AI. It allows programmers to create software agents that learn to take optimal actions to maximize reward, through trying out different strategies in a given environment.
This course will take you through all the core concepts in Reinforcement Learning, transforming a theoretical subject into tangible Python coding exercises with the help of OpenAI Gym. The videos will first guide you through the gym environment, solving the CartPole-v0 toy robotics problem, before moving on to coding up and solving a multi-armed bandit problem in Python. As the course ramps up, it shows you how to use dynamic programming and TensorFlow-based neural networks to solve GridWorld, another OpenAI Gym challenge. Lastly, we take the Blackjack challenge and deploy model free algorithms that leverage Monte Carlo methods and Temporal Difference (TD, more specifically SARSA) techniques.
The scope of Reinforcement Learning applications outside toy examples is immense. Reinforcement Learning can optimize agricultural yield in IoT powered greenhouses, and reduce power consumption in data centers. It’s grown in demand to the point where its applications range from controlling robots to extracting insights from images and natural language data. By the end of this course, you will not only be able to solve these problems but will also be able to use Reinforcement Learning as a problem-solving strategy and use different algorithms to solve these problems.
When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.
These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level.
Reinforcement learning has recently become popular for doing all of that and more.
Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible.
In 2016 we saw Google’s AlphaGo beat the world Champion in Go.
We saw AIs playing video games like Doom and Super Mario.
Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.
If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.
Best Reinforcement learning Books:
#1 Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series) 1st Edition by Richard S. Sutton & Andrew G. Barto & Francis Bach
#2 Practical Reinforcement Learning: Develop self-evolving, intelligent agents with OpenAI Gym, Python and Java by Dr. Engr. S.M. Farrukh Akhtar
#3 Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) second edition Edition by Richard S. Sutton & Andrew G. Barto & Francis Bach
#4 Reinforcement Learning: State-of-the-Art (Adaptation, Learning, and Optimization) by Marco Wiering & Martijn van Otterlo
#5 Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more by Maxim Lapan
#6 Reinforcement Learning and Dynamic Programming Using Function Approximators (Automation and Control Engineering) 1st Edition by Lucian Busoniu & Robert Babuska & Bart De Schutter