56 citations · 265 across the 35 of their papers we have counts for
7 papers · 1 filter
Gamma-Nets: Generalizing Value Estimation over Timescale
Craig Sherstan, Shibhansh Dohare, James MacGlashan +2
We present -nets, a method for generalizing value function estimation over timescale. By using the timescale as one of the estimator's inputs we can estimate value for arbitrary…
Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures
Johannes Günther, Nadia M. Ady, Alex Kearney +2
Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitat…
Learned human-agent decision-making, communication and joint action in a virtual reality environment
Patrick M. Pilarski, Andrew Butcher, Michael Johanson +3
Humans make decisions and act alongside other humans to pursue both short-term and long-term goals. As a result of ongoing progress in areas such as computing science and automatio…
Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison
Johannes Günther, Elias Reichensdörfer, Patrick M. Pilarski +1
Modern automation systems rely on closed loop control, wherein a controller interacts with a controlled process, based on observations. These systems are increasingly complex, yet…
When is a Prediction Knowledge?
Alex Kearney, Patrick M. Pilarski
Within Reinforcement Learning, there is a growing collection of research which aims to express all of an agent's knowledge of the world through predictions about sensation, behavio…
Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning
Sandy H. Huang, Martina Zambelli, Jackie Kay +4
Robots must know how to be gentle when they need to interact with fragile objects, or when the robot itself is prone to wear and tear. We propose an approach that enables deep rein…
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning
Alex Kearney, Vivek Veeriah, Jaden Travnik +2
There is a long history of using meta learning as representation learning, specifically for determining the relevance of inputs. In this paper, we examine an instance of meta-learn…