Search Results for author: Alex Kearney

Found 8 papers, 0 papers with code

Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making

no code implementations18 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.

Decision Making

What's a Good Prediction? Challenges in evaluating an agent's knowledge

no code implementations23 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.

Continual Learning General Knowledge

Women, politics and Twitter: Using machine learning to change the discourse

no code implementations25 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.

BIG-bench Machine Learning Decision Making

Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures

no code implementations15 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.

Representation Learning

Making Meaning: Semiotics Within Predictive Knowledge Architectures

no code implementations18 Apr 2019 Alex Kearney, Oliver Oxton

Within Reinforcement Learning, there is a fledgling approach to conceptualizing the environment in terms of predictions.

reinforcement-learning Reinforcement Learning (RL)

When is a Prediction Knowledge?

no code implementations18 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.

Decision Making reinforcement-learning +1

Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning

no code implementations8 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.

Meta-Learning Representation Learning

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