32,908 research outputs found
Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks
This paper discusses a system that accelerates reinforcement learning by
using transfer from related tasks. Without such transfer, even if two tasks are
very similar at some abstract level, an extensive re-learning effort is
required. The system achieves much of its power by transferring parts of
previously learned solutions rather than a single complete solution. The system
exploits strong features in the multi-dimensional function produced by
reinforcement learning in solving a particular task. These features are stable
and easy to recognize early in the learning process. They generate a
partitioning of the state space and thus the function. The partition is
represented as a graph. This is used to index and compose functions stored in a
case base to form a close approximation to the solution of the new task.
Experiments demonstrate that function composition often produces more than an
order of magnitude increase in learning rate compared to a basic reinforcement
learning algorithm
Application of Time-Fractional Order Bloch Equation in Magnetic Resonance Fingerprinting
Magnetic resonance fingerprinting (MRF) is one novel fast quantitative
imaging framework for simultaneous quantification of multiple parameters with
pseudo-randomized acquisition patterns. The accuracy of the resulting
multi-parameters is very important for clinical applications. In this paper, we
derived signal evolutions from the anomalous relaxation using a fractional
calculus. More specifically, we utilized time-fractional order extension of the
Bloch equations to generate dictionary to provide more complex system
descriptions for MRF applications. The representative results of phantom
experiments demonstrated the good accuracy performance when applying the
time-fractional order Bloch equations to generate dictionary entries in the MRF
framework. The utility of the proposed method is also validated by in-vivo
study.Comment: Accepted at 2019 IEEE 16th International Symposium on Biomedical
Imaging (ISBI 2019
- …