Deep Reinforcement Learning: Pong from Pixels

This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of these advances fall under the umbrella of RL research. I also became interested in RL myself over the last ~year: I worked through Richard Sutton’s book, read through David Silver’s course, watched John Schulmann’s lectures, wrote an RL library in Javascript, over the summer interned at DeepMind working in the DeepRL group, and most recently pitched in a little with the design/development of OpenAI Gym, a new RL benchmarking toolkit. Examples of RL in the wild. It’s interesting to reflect on the nature of recent progress in RL. Now back to RL. Pong from pixels Left: The game of Pong. Learning.

http://karpathy.github.io/2016/05/31/rl/

Related: Deep Learning • RL

Image Classification using Deep Neural Networks — A beginner friendly approach using TensorFlow Note: You can find the entire source code on this GitHub repo tl;dr We will build a deep neural network that can recognize images with an accuracy of 78.4% while explaining the techniques used throughout the process. Introduction A (Long) Peek into Reinforcement Learning In this post, we are gonna briefly go over the field of Reinforcement Learning (RL), from fundamental concepts to classic algorithms. Hopefully, this review is helpful enough so that newbies would not get lost in specialized terms and jargons while starting. [WARNING] This is a long read. A couple of exciting news in Artificial Intelligence (AI) has just happened in recent years.

Deep_Learning_Project ""Sometimes, we tend to get lost in the jargon and confuse things easily, so the best way to go about this is getting back to our basics. Don’t forget what the original premise of machine learning (and thus deep learning) is - IF the input and output are related by a function y=f(x), then if we have x, there is no way to exactly know f unless we know the process itself. However, machine learning gives you the ability to approximate f with a function g, and the process of trying out multiple candidates to identify the function g best approximating f is called machine learning. Ok, that was machine learning, and how is deep learning different? Deep learning simply tries to expand the possible kind of functions that can be approximated using the above mentioned machine learning paradigm.

Sutton & Barto Book: Reinforcement Learning: An Introduction Second Edition (see herefor the first edition) MIT Press, Cambridge, MA, 2018 Buy from Amazon ErrataFull Pdf pdf without margins (good for ipad)New Code Old Code Solutions -- send in your solutions for a chapter, get the official ones back (currently incomplete)Teaching AidsLiterature sources cited in the book Latex Notation -- Want to use the book's notation in your own work? Download this .sty file and this example of its use Help out! If you enjoyed the book, why not give back to the community? Deep Learning with Python Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc.

Rendering OpenAi Gym in Google Colaboratory. – StarAi – StarAi Applied Research Blog By: Paul Steven Conyngham Early this year (2018) Google introduced free GPUs to their machine learning tool “Colaboratory”, making it the perfect platform for doing machine learning work or research. If you are looking at getting started with Reinforcement Learning however, you may have also heard of a tool released by OpenAi in 2016, called “OpenAi Gym”. Learning Deep Learning with Keras I teach deep learning both for a living (as the main deepsense.io instructor, in a Kaggle-winning team) and as a part of my volunteering with the Polish Children’s Fund giving workshops to gifted high-school students. I want to share a few things I’ve learnt about teaching (and learning) deep learning. Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you! Its goal is not to teach neural networks by itself, but to provide an overview and to point to didactically useful resources. Don’t be afraid of artificial neural networks - it is easy to start!

Deep Reinforcement Learning with TensorFlow 2.0 In this tutorial I will showcase the upcoming TensorFlow 2.0 features through the lense of deep reinforcement learning (DRL) by implementing an advantage actor-critic (A2C) agent to solve the classic CartPole-v0 environment. While the goal is to showcase TensorFlow 2.0, I will do my best to make the DRL aspect approachable as well, including a brief overview of the field. In fact since the main focus of the 2.0 release is making developers’ lives easier, it’s a great time to get into DRL with TensorFlow - our full agent source is under 150 lines!

The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) – Adit Deshpande – CS Undergrad at UCLA ('19) Introduction Link to Part 1 Link to Part 2 In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and convolutional neural networks. We’ll look at some of the most important papers that have been published over the last 5 years and discuss why they’re so important. The first half of the list (AlexNet to ResNet) deals with advancements in general network architecture, while the second half is just a collection of interesting papers in other subareas.

The Promise of Hierarchical Reinforcement Learning Update: Jürgen Schmidhuber kindly suggested some corrections concerning the early work on intrinsic motivation, subgoal discovery and artificial curiosity since 1990, which I have incorporated and expanded. Suppose your friend just baked and shared an excellent cake with you, and you would like to know its recipe. It might seem that it should be very easy for your friend to just tell you how to cook the cake — that it should be easy for him to get across the recipe. But this is a subtler task than you might think; how detailed should the instructions be?

Coding the History of Deep Learning - FloydHub Blog There are six snippets of code that made deep learning what it is today. This article covers the inventors and the background to their breakthroughs. Each story includes simple code samples on FloydHub and GitHub to play around with. Deep Reinforcement Learning: Playing CartPole through Asynchronous Advantage Actor Critic (A3C)… By Raymond Yuan, Software Engineering Intern In this tutorial we will learn how to train a model that is able to win at the simple game CartPole using deep reinforcement learning. We’ll use tf.keras and OpenAI’s gym to train an agent using a technique known as Asynchronous Advantage Actor Critic (A3C). Reinforcement learning has been receiving an enormous amount of attention, but what is it exactly? Reinforcement learning is an area of machine learning that involves agents that should take certain actions from within an environment to maximize or attain some reward. In the process, we’ll build practical experience and develop intuition around the following concepts: