| 1 | \begin{thebibliography}{10} |
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| 2 | \providecommand{\url}[1]{\texttt{#1}} |
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| 3 | \providecommand{\urlprefix}{URL } |
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| 4 | \expandafter\ifx\csname urlstyle\endcsname\relax |
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| 5 | \providecommand{\doi}[1]{doi:\discretionary{}{}{}#1}\else |
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| 6 | \providecommand{\doi}{doi:\discretionary{}{}{}\begingroup |
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| 7 | \urlstyle{rm}\Url}\fi |
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| 8 | \providecommand{\selectlanguage}[1]{\relax} |
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| 9 | \providecommand{\eprint}[2][]{\url{#2}} |
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| 10 | |
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| 11 | \bibitem{aimsunget} |
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| 12 | \emph{AIMSUN Getram v4.2 getting started - User's manual}. 2003. |
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| 13 | |
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| 14 | \bibitem{7_lq_methods} |
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| 15 | Anderson, M.~J., B.D.O.: Optimal Control - Linear Quadratic Methods. |
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| 16 | \emph{Prentice Hall, Englewood Cliffs, NJ}, 1990. |
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| 19 | Bellman, R.: Dynamic programming. \emph{Princeton University Press}, 1957. |
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| 20 | |
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| 21 | \bibitem{2_int_a_in_dec} |
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| 23 | decentralized traffic control. \emph{Intelligent Transportation Systems}, |
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| 24 | 2001. |
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| 26 | \bibitem{4_rmm_formalization} |
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| 27 | Gmytrasiewicz, P.~J.; Durfee, E.~H.: A rigorous, operational formalization of |
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| 28 | recursive modeling. \emph{First International Conference on Multiagent |
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| 29 | Systems}, 1995. |
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| 31 | \bibitem{5_bayes_learn} |
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| 32 | Nagy, I.; Nedoma, P.; Ettler, P.; aj.: O bayesovsk{\'e}m u\v{c}en{\'i}. |
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| 33 | \emph{Automa}, 2002. |
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| 35 | \bibitem{1_rmm_bayes_learning} |
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| 36 | Ou, H.; Zhang, W.; Xu, X.: Urban traffic multi-agent system based on RMM and |
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| 37 | Bayesian learning. \emph{American Control Conference}, 2002. |
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| 39 | \bibitem{17_fronta} |
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| 40 | Pecherkov{\'a}, P.; Dun{\'i}k, J.; Fl{\'i}dr, M.: Modelling and Simultaneous |
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| 41 | Estimation of State and Parameters of Traffic System. \emph{Robotics, |
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| 42 | Automation and Control}, 2008. |
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| 44 | \bibitem{lqg_parallel} |
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| 45 | Schier, J.: Parallel algorithms for Robust Adaptive Identification and |
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| 46 | Square-Root LQG Controll Synthesis. 1994. |
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| 47 | |
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| 48 | \bibitem{learning_to_predict} |
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| 49 | Sutton, R.~S.: Learning to predict by the methods of temporal didffrences. |
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| 50 | \emph{Machine Learning}, 1988. |
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| 52 | \bibitem{tlc_using_sarsa} |
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| 53 | Thorpe, T.: Vehicle traffic light controlusing sarsa. \emph{Master’s thesis, |
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| 54 | Department of Computer Science, Colorado State University}, 1997. |
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| 55 | |
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| 56 | \bibitem{6_tuc_lq} |
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| 57 | Vaya~Dinopoulou, M.~P., Christina~Diakaki: Applications of the urban traffic |
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| 58 | control strategy TUC. \emph{European Journal of Operational Research}, 2005. |
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| 60 | \bibitem{leraning_from_delayed_rewards} |
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| 61 | Watkins, C. J. C.~H.: Leraning from Delayed Rewards. \emph{PhD thesis, King's |
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| 62 | College, Cambridge, England}, 1989. |
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| 65 | Watkins, C. J. C.~H.; Dayan, P.: Q-leraning. \emph{Machine Learning}, 1992. |
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| 67 | \bibitem{3_i_traff_light_c} |
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| 68 | Wiering, M.; {Van Veenen}, J.; Vreeken, J.; aj.: Intelligent traffic light |
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| 69 | control. \emph{European Research Consortium for Informatics and Mathematics}, |
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| 70 | 2003. |
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| 71 | |
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| 72 | \bibitem{wooldridge} |
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| 73 | Wooldridge, M.: \emph{Multi Agent Systems}. MIT Press, Březen 2005. |
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| 74 | |
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| 75 | \end{thebibliography} |
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