Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize some notion of cumulative reward.
Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. source:wiki
List of free reinforcement learning courses resources online in 2020
Foundations of RL methods:
value/policy iteration, q-learning, policy gradient, etc. — with math & batteries included – using deep neural networks for RL tasks — also known as “the hype train” – state of the art RL algorithms — and how to apply duct tape to them for practical problems.
– and, of course, teaching your neural network to play games — because that’s what everyone thinks RL is about.
Use it for seq2seq and contextual bandits.
Learn how to frame reinforcement learning problems, tackle classic examples, explore basic algorithms from dynamic programming, temporal difference learning, and progress towards larger state space using function approximation and DQN (Deep Q Network).
This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management.
The course is not being offered as an online course, and the videos are provided only for your personal informational and entertainment purposes. They are not part of any course requirement or degree-bearing university program.
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.
This course is a series of articles and videos where you’ll master the skills and architectures you need, to become a deep reinforcement learning expert.
You’ll build a strong professional portfolio by implementing awesome agents with Tensorflow that learns to play Space invaders, Doom, Sonic the hedgehog and more!
Deep reinforcement learning combines artificial neural networks with a reinforcement learning architecture that enables software-defined agents to learn the best actions possible in virtual environment in order to attain their goals.
That is, it unites function approximation and target optimization, mapping state-action pairs to expected rewards.
This course assumes some familiarity with reinforcement learning, numerical optimization, and machine learning. Suggested relevant courses in MLD are 10701 Introduction to Machine Learning
In recent years, reinforcement learning has been combined with deep neural networks, giving rise to game agents with super-human performance (for example for Go, chess, or 1v1 Dota2, capable of being trained solely by self-play), datacenter cooling algorithms being 50% more efficient than trained human operators, or improved machine translation.
The goal of the course is to introduce reinforcement learning employing deep neural networks, focusing both on the theory and on practical implementations.
This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).
This module contains a variety of helpful resources, including:
- a short introduction to RL terminology, kinds of algorithms, and basic theory,
- an essay about how to grow into an RL research role,
- a curated list of important papers organized by topic,
- a well-documented code repo of short, standalone implementations of key algorithms,
- and a few exercises to serve as warm-ups.
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Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect.
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