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Found 8 papers, 0 papers with code
Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making
In computational reinforcement learning, a growing body of work seeks to express an agent's model of the world through predictions about future sensations.
What's a Good Prediction? Challenges in evaluating an agent's knowledge
Constructing general knowledge by learning task-independent models of the world can help agents solve challenging problems.
Women, politics and Twitter: Using machine learning to change the discourse
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.
Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures
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.
Making Meaning: Semiotics Within Predictive Knowledge Architectures
Within Reinforcement Learning, there is a fledgling approach to conceptualizing the environment in terms of predictions.
When is a Prediction Knowledge?
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.
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning
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.
TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent
In this paper, we introduce a method for adapting the step-sizes of temporal difference (TD) learning.
