Geometric symmetry in graphs
Online minimization of transition systems (extended abstract)
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Graph isomorphism, general remarks
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
An algebraic approach to abstraction in reinforcement learning
An algebraic approach to abstraction in reinforcement learning
Multiagent learning in the presence of agents with limitations
Multiagent learning in the presence of agents with limitations
Algebraic structure theory of sequential machines (Prentice-Hall international series in applied mathematics)
Model minimization in Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Exploiting symmetries for single- and multi-agent Partially Observable Stochastic Domains
Artificial Intelligence
| Hi-index | 0.00 |
In this work we address the question of finding symmetries of a given MDP. We show that the problem is Isomorphism Complete, that is, the problem is polynomially equivalent to verifying whether two graphs are isomorphic. Apart from the theoretical importance of this result it has an important practical application. The reduction presented can be used together with any off-the-shelf Graph Isomorphism solver, which performs well in the average case, to find symmetries of an MDP. In fact, we present results of using NAutY (the best Graph Isomorphism solver currently available), to find symmetries of MDPs.