A typical process of learning to play a piece on a piano consists of a progression through a series of practice units that focus on individual dimensions of the skill, the so-called practice modes. Practice modes in learning to play music comprise a particularly large set of possibilities, such as hand coordination, posture, articulation, ability to read a music score, correct timing or pitch, etc. Self-guided practice is known to be suboptimal, and a model that schedules optimal practice to maximize a learner's progress still does not exist. Because we each learn differently and there are many choices for possible piano practice tasks and methods, the set of practice modes should be dynamically adapted to the human learner, a process typically guided by a teacher. However, having a human teacher guide individual practice is not always feasible since it is time-consuming, expensive, and often unavailable. In this work, we present a modeling framework to guide the human learner through the learning process by choosing the practice modes generated by a policy model. To this end, we present a computational architecture building on a Gaussian process that incorporates 1) the learner state, 2) a policy that selects a suitable practice mode, 3) performance evaluation, and 4) expert knowledge. The proposed policy model is trained to approximate the expert-learner interaction during a practice session. In our future work, we will test different Bayesian optimization techniques, e.g., different acquisition functions, and evaluate their effect on the learning progress.

A code tutorial in Tensorflow that uses Reinforcement Learning to take free kicks. In my previous article, I presented an AI bot trained to play the game of FIFA using Supervised Learning technique. However, the training data required to improve it further quickly became cumbersome to gather and provided little-to-no improvements, making this approach very time consuming. For this sake, I decided to switch to Reinforcement Learning, as suggested by almost everyone who commented on that article! In this article, I'll provide a short description of what Reinforcement Learning is and how I applied it to this game.

Before I wrote the JavaScripts, I got a master's in AI (almost a decade ago), and wrote a thesis on a weird and new area in Reinforcement Learning. Or at least it was new then. With all the hype around Machine Learning and Deep Learning, I thought it would be neat if I wrote a little primer on what Reinforcement Learning really means, and why it's different than just another neural net. Richard Sutton and Andrew Barto wrote an amazing book called "Reinforcement Learning: an introduction"; it's my favourite non-fiction book I have ever read in my life, and it's why I fell in love with RL. The complete draft is available for free here, and if you're into math, and want to explore this topic further, I can't recommend it enough.