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Found 6 papers, 0 papers with code
Unconstrained Online Learning with Unbounded Losses
Algorithms for online learning typically require one or more boundedness assumptions: that the domain is bounded, that the losses are Lipschitz, or both.
Parameter-free Mirror Descent
We develop a modified online mirror descent framework that is suitable for building adaptive and parameter-free algorithms in unbounded domains.
Continual Auxiliary Task Learning
Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems.
Parameter-free Gradient Temporal Difference Learning
In parallel, progress in online learning has provided parameter-free methods that achieve minimax optimal guarantees up to logarithmic terms, but their application in reinforcement learning has yet to be explored.
Meta-descent for Online, Continual Prediction
This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems.
General Value Function Networks
A general purpose strategy for state construction is to learn the state update using a Recurrent Neural Network (RNN), which updates the internal state using the current internal state and the most recent observation.
