Collaborating Authors

Reinforcement Learning in Motion


Reinforcement Learning in Motion introduces you to the exciting world of machine systems that learn from their environments! In this course, he'll break down key concepts like how RL systems learn, how to sense and process environmental data, and how to build and train AI agents. As you learn, you'll master the core algorithms and get to grips with tools like Open AI Gym, numpy, and Matplotlib. Reinforcement systems learn by doing, and so will you in this interactive, hands-on course! You'll build and train a variety of algorithms as you go, each with a specific purpose in mind.


AAAI Conferences

Most of the existing weight-learning algorithms for Markov Logic Networks (MLNs) use batch training which becomes computationally expensive and even infeasible for very large datasets since the training examples may not fit in main memory. To overcome this problem, previous work has used online learning algorithms to learn weights for MLNs. However, this prior work has only applied existing online algorithms, and there is no comprehensive study of online weight learning for MLNs. In this paper, we derive new online algorithms for structured prediction using the primal-dual framework, apply them to learn weights for MLNs, and compare against existing online algorithms on two large, real-world datasets. The experimental results show that the new algorithms achieve better accuracy than existing methods.

[P] Making a robot learn how to move, part 1 -- Evolutionary algorithms • r/MachineLearning


This is part of a project I've been working in that involves using ML techniques to robot control. The first one is applying evolutionary algorithms to a neural controller. You cna find a Jupyter Notebook on the linked repository.

Deep learning algorithm learns how to frighten us


Just in time for Halloween, researchers at Data61 and MIT Media lab have created a deep learning algorithm to generate disturbing imagery. There are two parts to the Nightmare Machine project – Haunted Places and Haunted Faces – which are each terrifying and impressive in equal measure. For Haunted Places the team used algorithms to learn what it called a'nightmarifying' process, learning a variety of spooky artistic styles that can then be applied to idyllic imagery. "We use deep learning algorithms to learn first how haunted houses, then ghost towns, and more recently toxic cities look," explains principal research scientist at Data61, Manuel Cebrian. "Then, we apply the learned style to famous landmarks.

Ready, Set, Algorithms! Teams Learn AI by Racing Cars


The DeepRacer league, developed by Amazon Web Services, is designed to teach a branch of artificial intelligence known as reinforcement learning. Amazon Web Services (AWS) has developed the DeepRacer League, a competition designed to teach a branch of artificial intelligence (AI) known as reinforcement learning, in which algorithms learn the correct way to perform an action based on trial and error, and observations. As part of the DeepRacer League, teams or individuals build and train AI algorithms using Amazon SageMaker software, then deploy them to self-driving model cars measuring about 10 inches long, which they race around a track roughly 17 feet by 26 feet. Morningstar is one of the companies participating in the DeepRacer League, and thanks to the training, the company expects to have dozens of projects based on reinforcement learning and other machine learning techniques in deployment by the end of next year. AWS developed the DeepRacer program in an effort to teach software developers about machine learning in a more engaging way than reading scientific articles.