In this paper, we propose a novel adaptive step-size approach for policy gradient reinforcement learning. A new metric is defined for policy gradients that measures the effect of ...
Takamitsu Matsubara, Tetsuro Morimura, Jun Morimot...
In this paper we describe ICARUS, an adaptive architecture for intelligent physical agents. We contrast the framework’s assumptions with those of earlier architectures, taking e...
We propose a novel self-training method for a parser which uses a lexicalised grammar and supertagger, focusing on increasing the speed of the parser rather than its accuracy. The...
Jonathan K. Kummerfeld, Jessika Roesner, Tim Dawbo...
We investigate whether erroneous examples in the domain of fractions can help students learn from common errors of other students presented in a computer-based system. Presenting t...
Dimitra Tsovaltzi, Erica Melis, Bruce M. McLaren, ...
We present the Recursive Least Squares Dictionary Learning Algorithm, RLSDLA, which can be used for learning overcomplete dictionaries for sparse signal representation. Most Dicti...