[1] R. S. Sutton, D. Precup, and S. Singh. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112(1-2):181-211, 1999.
[2] D. Precup. Temporal abstraction in reinforcement learning. PhD thesis, University of Massachusetts Amherst, 2000.
[3] M. E. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review E, 69(2):026113, 2004.
[4] E. A. Leicht and M. E. Newman. Community structure in directed networks. Physical Review Letters, 100(11):118703, 2008.
[5] A. Arenas, J. Duch, A. Fernández, and S. Gómez. Size reduction of complex networks preserving modularity. New Journal of Physics, 9(6):176, 2007.
[6] U. Brandes, D. Delling, M. Gaertler, R. Gorke, M. Hoefer, Z. Nikoloski, and D. Wagner. Maximizing modularity is hard. arXiv preprint:physics/0608255, 2006.
[7] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):10008, 2008.
[8] A. Lancichinetti and S. Fortunato. Community detection algorithms: a comparative analysis. Physical Review E, 80(5):056117, 2009.
[9] I. Menache, S. Mannor, and N. Shimkin. Q-Cut — Dynamic discovery of sub-goals in reinforcement learning. In Proceedings of the 13th European Conference on Machine Learning, ECML '02, pages 295–306. Springer-Verlag, 2002.
[10] Ö. Şimşek and A. G. Barto. Using relative novelty to identify useful temporal abstractions in reinforcement learning. In Proceedings of the 21st International Conference on Machine Learning, ICML '04, pages 95-102. ACM, 2004.
[11] Ö. Şimşek and A. G. Barto. Skill characterization based on betweenness. In Advances in Neural Information Processing Systems 21, NeurIPS '09, pages 1497-1504. Curran Associates, Inc., 2009.
[12] P. Moradi, M. E. Shiri, and N. Entezari. Automatic skill acquisition in reinforcement learning agents using connection bridge centrality. In International Conference on Future Generation Communication and Networking, pages 51-62. Springer, 2010.
[13] A. A. Rad, M. Hasler, and P. Moradi. Automatic skill acquisition in reinforcement learning using connection graph stability centrality. In Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pages 697-700. IEEE, 2010.
[14] M. A. Imanian and P. Moradi. Autonomous subgoal discovery in reinforcement learning agents using bridgeness centrality measure. International Journal of Electrical and Computer Sciences, 11(5):54-62, 2011.
[15] P. Moradi, M. E. Shiri, A. A. Rad, A. Khadivi, and M. Hasler. Automatic skill acquisition in reinforcement learning using graph centrality measures. Intelligent Data Analysis, 16(1): 113-135, 2012.
[16] J. H. Metzen. Online skill discovery using graph-based clustering. In Proceedings of the Tenth European Workshop on Reinforcement Learning, EWRL '13, pages 77-88. PMLR, 2013.
[17] Ö. Şimsek, A. P. Wolfe, and A. G. Barto. Identifying useful subgoals in reinforcement learning by local graph partitioning. In Proceedings of the 22nd International Conference on Machine learning, ICML '05, pages 816-823. ACM, 2005.
[18] S. J. Kazemitabar and H. Beigy. Using strongly connected components as a basis for autonomous skill acquisition in reinforcement learning. In 6th International Symposium on Neural Networks, ISSN '09, pages 794-803. Springer, 2009.