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Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions
Online Learning Markov Decision Processes Adversarially Chosen Transition Probability Distributions
2013/5/2
We study the problem of learning Markov decision processes with finite state and action spaces when the transition probability distributions and loss functions are chosen adversarially and are allowed...
Second-Order Non-Stationary Online Learning for Regression
Second-Order Non-Stationary Online Learning for Regression
2013/4/28
The goal of a learner, in standard online learning, is to have the cumulative loss not much larger compared with the best-performing function from some fixed class. Numerous algorithms were shown to h...