RMRL: improved regret minimisation techniques using learning automata

References

• Antonio, L. M., & Coello, C. A. C. (2017). Coevolutionary multi-objective evolutionary algorithms: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 1.
   PubMed Web of Science ®Google Scholar
• Banerjee, B., & Peng, J. (2012). Strategic best-response learning in multiagent systems. Journal of Experimental & Theoretical Artificial Intelligence, 24(2), 139–160.
   Web of Science ®Google Scholar
• Berryman, A. A. (1992). The orgins and evolution of predator‐prey theory. Ecology, 73(5), 1530–1535.
   Web of Science ®Google Scholar
• Chowdhury, S., Dulikravich, G. S., & Moral, R. J. (2009). Modified predator-prey algorithm for constrained and unconstrained multi-objective optimisation. International Journal of Mathematical Modelling and Numerical Optimisation, 1(1–2), 1–38.
   Google Scholar
• Ferreira, L. A., Ribeiro, C. H. C., & Da Costa Bianchi, R. A. (2014). Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems. Applied Intelligence, 41(2), 551–562.
   Web of Science ®Google Scholar
• Foster, D. P., & Vohra, R. (1999). Regret in the on-line decision problem. Games and Economic Behavior, 29(1–2), 7–35.
   Web of Science ®Google Scholar
• Fudenberg, D., & Tirole, J. (1991). Game theory, 1991. Cambridge, Massachusetts, 393(12), 80.
   Google Scholar
• Harsanyi, J. C. (1967). Games with incomplete information played by “bayesian” players, i–iii part i. the basic model. The Basic Model. Management Science, 14(3), 159-182. doi:10.1287/mnsc.14.3.159
   Google Scholar
• Hart, S., & Mas‐Colell, A. (2000). A simple adaptive procedure leading to correlated equilibrium. Econometrica, 68(5), 1127–1150.
   Web of Science ®Google Scholar
• Hensher, D. A., Greene, W. H., & Ho, C. Q. (2016). Random regret minimization and random utility maximization in the presence of preference heterogeneity: An empirical contrast. Journal of Transportation Engineering, 142(4), 4016009.
   Google Scholar
• Jalalimanesh, A., Haghighi, H. S., Ahmadi, A., Hejazian, H., & Soltani, M. (2017). Multi-objective optimization of radiotherapy: Distributed Q-learning and agent-based simulation. Journal of Experimental & Theoretical Artificial Intelligence, 29(5), 1071–1086.
   Web of Science ®Google Scholar
• Myerson, R. (1991). Game Theory: Analysis of Conflict. Cambridge: Harvard University Press.
   Google Scholar
• Narendra, K., & Thathachar, M. A. L. (1989). Learning automata: an introduction. NJ:Prentice-Hall, Englewood Cli s.
   Google Scholar
• Narendra, K. S., & Parthasarathy, K. (1991). Learning automata approach to hierarchical multiobjective analysis. IEEE Transactions on Systems, Man, and Cybernetics, 21(1), 263–272.
   Web of Science ®Google Scholar
• Oremland, M., & Laubenbacher, R. (2015). Optimal harvesting for a predator-prey agent-based model using difference equations. Bulletin of Mathematical Biology, 77(3), 434–459.
   PubMed Web of Science ®Google Scholar
• Pettersson, F., Chakraborti, N., & Saxén, H. (2007). A genetic algorithms based multi-objective neural net applied to noisy blast furnace data. Applied Soft Computing, 7(1), 387–397.
   Web of Science ®Google Scholar
• Poznyak, A. S., & Najim, K. (1997). Learning automata and stochastic optimization. Lecture Notes in Control and Information Sciences (225), Springer-Verlag, London.
   Google Scholar
• Shoham, Y., & Leyton-Brown, K. (2008). Multiagent systems: Algorithmic, game-theoretic, and logical foundations. Cambridge University Press.
   Google Scholar
• Sutton, R. S., & Barto, A. G. (2011). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
   Google Scholar
• Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.
   Web of Science ®Google Scholar
• Zinkevich, M., Johanson, M., Bowling, M., & Piccione, C. (2007). Regret minimization in games with incomplete information. In Advances in neural information processing systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems (pp. 1729-1736).
   Google Scholar