Bisimulation through probabilistic testing
Information and Computation
Elements of information theory
Elements of information theory
Technical Note: \cal Q-Learning
Machine Learning
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Finite-sample convergence rates for Q-learning and indirect algorithms
Proceedings of the 1998 conference on Advances in neural information processing systems II
Parallel Optimization: Theory, Algorithms and Applications
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Introduction to Reinforcement Learning
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CONCUR '90 Proceedings of the Theories of Concurrency: Unification and Extension
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Bisimulation for Labelled Markov Processes
LICS '97 Proceedings of the 12th Annual IEEE Symposium on Logic in Computer Science
Dynamic Programming
Design and analysis of efficient reinforcement learning algorithms
Design and analysis of efficient reinforcement learning algorithms
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Testing probabilistic equivalence through reinforcement learning
FSTTCS'06 Proceedings of the 26th international conference on Foundations of Software Technology and Theoretical Computer Science
Testing probabilistic equivalence through Reinforcement Learning
Information and Computation
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In the context of probabilistic verification, we provide a new notion of trace-equivalence divergence between pairs of Labelled Markov processes. This divergence corresponds to the optimal value of a particular derived Markov Decision Process. It can therefore be estimated by Reinforcement Learning methods. Moreover, we provide some PAC-guarantees on this estimation.