Bisimulation through probabilistic testing
Information and Computation
Elements of information theory
Elements of information theory
Technical Note: \cal 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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Testing Labelled Markov Processes
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Equivalences, Congruences, and Complete Axiomatizations for Probabilistic Processes
CONCUR '90 Proceedings of the Theories of Concurrency: Unification and Extension
The Linear Time - Branching Time Spectrum II
CONCUR '93 Proceedings of the 4th International Conference on Concurrency Theory
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
Design and analysis of efficient reinforcement learning algorithms
Design and analysis of efficient reinforcement learning algorithms
Approximating and computing behavioural distances in probabilistic transition systems
Theoretical Computer Science
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Trace equivalence characterization through reinforcement learning
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Learning the Difference between Partially Observable Dynamical Systems
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Continuous time and/or continuous distributions
EPEW'10 Proceedings of the 7th European performance engineering conference on Computer performance engineering
Testing probabilistic equivalence through Reinforcement Learning
Information and Computation
| Hi-index | 0.00 |
We propose a new approach to verification of probabilistic processes for which the model may not be available. We use a technique from Reinforcement Learning to approximate how far apart two processes are by solving a Markov Decision Process. If two processes are equivalent, the algorithm will return zero, otherwise it will provide a number and a test that witness the non equivalence. We suggest a new family of equivalences, called K-moment, for which it is possible to do so. The weakest, 1-moment equivalence, is trace-equivalence. The others are weaker than bisimulation but stronger than trace-equivalence.