Off-Policy Prediction Learning: An Empirical Study of Online Algorithms.

Off-policy prediction-learning the value function for one policy from data generated while following another policy-is one of the most challenging problems in reinforcement learning. This article makes two main contributions: 1) it empirically studies 11 off-policy prediction learning algorithms with emphasis on their sensitivity to parameters, learning speed, and asymptotic error and 2) based on the empirical results, it proposes two step-size adaptation methods called and that help the algorithm with the lowest error from the experimental study learn faster. Many off-policy prediction learning algorithms have been proposed in the past decade, but it remains unclear which algorithms learn faster than others.

From eye-blinks to state construction: Diagnostic benchmarks for online representation learning.

We present three new diagnostic prediction problems inspired by classical-conditioning experiments to facilitate research in online prediction learning. Experiments in classical conditioning show that animals such as rabbits, pigeons, and dogs can make long temporal associations that enable multi-step prediction. To replicate this remarkable ability, an agent must construct an internal state representation that summarizes its interaction history.

Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism.

A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets.