no code implementations • 18 Nov 2021 • Alex Kearney, Anna Koop, Johannes Günther, Patrick M. Pilarski
In computational reinforcement learning, a growing body of work seeks to express an agent's model of the world through predictions about future sensations.
no code implementations • 23 Jan 2020 • Alex Kearney, Anna Koop, Patrick M. Pilarski
Constructing general knowledge by learning task-independent models of the world can help agents solve challenging problems.
no code implementations • 25 Nov 2019 • Lana Cuthbertson, Alex Kearney, Riley Dawson, Ashia Zawaduk, Eve Cuthbertson, Ann Gordon-Tighe, Kory W. Mathewson
In this paper, we present ParityBOT: a Twitter bot which counters abusive tweets aimed at women in politics by sending supportive tweets about influential female leaders and facts about women in public life.
no code implementations • 15 Aug 2019 • Johannes Günther, Nadia M. Ady, Alex Kearney, Michael R. Dawson, Patrick M. Pilarski
Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitation.
no code implementations • 18 Apr 2019 • Alex Kearney, Oliver Oxton
Within Reinforcement Learning, there is a fledgling approach to conceptualizing the environment in terms of predictions.
no code implementations • 18 Apr 2019 • Alex Kearney, Patrick M. Pilarski
While promising, we here suggest that the notion of predictions as knowledge in reinforcement learning is as yet underdeveloped: some work explicitly refers to predictions as knowledge, what the requirements are for considering a prediction to be knowledge have yet to be well explored.
no code implementations • 8 Mar 2019 • Alex Kearney, Vivek Veeriah, Jaden Travnik, Patrick M. Pilarski, Richard S. Sutton
In this paper, we examine an instance of meta-learning in which feature relevance is learned by adapting step size parameters of stochastic gradient descent---building on a variety of prior work in stochastic approximation, machine learning, and artificial neural networks.
no code implementations • 10 Apr 2018 • Alex Kearney, Vivek Veeriah, Jaden B. Travnik, Richard S. Sutton, Patrick M. Pilarski
In this paper, we introduce a method for adapting the step-sizes of temporal difference (TD) learning.