31 citations · 33 across the 6 of their papers we have counts for
6 papers · 1 filter
What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience
Alexandra Kearney, Anna Koop, Johannes Günther +1
In computational reinforcement learning, a growing body of work seeks to construct an agent's perception of the world through predictions of future sensations; predictions about en…
Prototyping three key properties of specific curiosity in computational reinforcement learning
Nadia M. Ady, Roshan Shariff, Johannes Günther +1
Curiosity for machine agents has been a focus of intense research. The study of human and animal curiosity, particularly specific curiosity, has unearthed several properties that w…
Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making
Alex Kearney, Anna Koop, Johannes Günther +1
In computational reinforcement learning, a growing body of work seeks to express an agent's model of the world through predictions about future sensations. In this manuscript we fo…
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…
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…