Markov Models
Probabilistic Logic Neural Networks for Reasoning
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Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs
Max Simchowitz, Kevin G. Jamieson
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Localized Structured Prediction
Carlo Ciliberto, Francis Bach, Alessandro Rudi
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NavigatingtotheBestPolicy inMarkovDecisionProcesses
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Somewhat surprisingly, learning in a Markov Decision Process is most often considered under the performance criteria ofconsistency or regret minimization(see e.g.
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Reward is Enough for Convex MDPs
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Maximising a cumulative reward function that is Markov and stationary, i.e., defined over state-action pairs and independent of time, is sufficient to capture many
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