Adaptive mixtures of probabilistic transducers
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
The optimal error exponent for Markov order estimation
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Optimal error exponents in hidden Markov models order estimation
IEEE Transactions on Information Theory
Feature reinforcement learning in practice
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
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We are studying long term sequence prediction (forecasting). We approach this by investigating criteria for choosing a compact useful state representation. The state is supposed to summarize useful information from the history. We want a method that is asymptotically consistent in the sense it will provably eventually only choose between alternatives that satisfy an optimality property related to the used criterion. We extend our work to the case where there is side information that one can take advantage of and, furthermore, we briefly discuss the active setting where an agent takes actions to achieve desirable outcomes.