Top.Mail.Ru
Мультиагентное обучение с подкреплением / Издательство МГТУ им. Н. Э. Баумана
Бумажная
Электронная
  • Формат: 70x100/16
  • Переплёт: мягкий
  • Год издания: 2021 г.
  • Объём: 224 стр.
  • Объём: 18.20 п.л.
  • Номер издания: 1
  • Вес: 371 г.
  • ISBN: 978-5-7038-5616-1
  • Формат: PDF
  • Объём: 224 стр.
  • Год издания: 2021 г.
  • Номер издания: 1
  • ISBN: 978-5-7038-5616-1

О книге

Рассмотрены современные и классические алгоритмы одновременного машинного обучения множества агентов, основанные на теории игр, табличных, нейросетевых, эволюционных и роевых технологиях. Представлено последовательное развитие теоретической модели алгоритмов, базирующееся на марковских процессах принятия решений. Реализация алгоритмов выполнена на языке программирования Python с использованием библиотеки глубокого обучения PyTorch. Средой машинного обучения является компьютерная игра StarCraft II с интерфейсом кооперативного мультиагентного обучения SMAC.

Для магистрантов и аспирантов направления подготовки «Информатика и вычислительная техника».
Список литературы
  1. Девятков В.В. Системы искусственного интеллекта: учеб. пособие для вузов. М.: Изд-во МГТУ им. Н.Э. Баумана, 2001. 352 с.
  2. Тарасов В.Б. От многоагентных систем к интеллектуальным организациям. М.: Едиториал УРСС, 2002. 353 с.
  3. Buşoniu L., Babuška R., De Schutter B. Multiagent reinforcement learning: An overview // Innovations in multi-agent systems and applications. Berlin: Springer, 2010. P. 183–221.
  4. Claus C., Boutilier C. The dynamics of reinforcement learning in cooperative multiagent systems // AAAI/IAAI. 1998. Vol. 2. P. 746–752.
  5. Greenwald A., Hall K., Serrano R. Correlated Q-learning // ICML. 2003. Vol. 20. No. 1. P. 242.
  6. Hernandez-Leal P. et al. A survey of learning in multiagent environments: Dealing with non-stationarity // URL: https://arxiv.org/abs/1707.09183 (дата об-ращения 01.11.2020).
  7. Klein D., Abbeel P. CS 188: Artificial Intelligence // URL: https://inst.eecs.berkeley.edu/~cs188/sp20 (дата обращения 01.11.2020).
  8. Könönen V. Asymmetric multiagent reinforcement learning // Web Intelligence and Agent Systems: An international journal. 2004. Vol. 2. No. 2. P. 105–121.
  9. Lauer M., Riedmiller M. An algorithm for distributed reinforcement learning in cooperative multiagent systems // Proceedings of the Seventeenth International Conference on Machine Learning. 2000.
  10. Laurent G.J. et al. The world of independent learners is not Markovian // International Journal of Knowledge-based and Intelligent Engineering Systems. 2011. Vol. 15. No. 1. P. 55–64.
  11. Leike J. et al. AI safety gridworlds. URL: https://arxiv.org/abs/1711.09883 (дата обращения 01.11.2020).
  12. Littman M.L. Value-function reinforcement learning in Markov games // Cognitive systems research. 2001. Vol. 2. No. 1. P. 55–66.
  13. Matignon L., Laurent G.J., Le Fort-Piat N. Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems // Knowledge Engineering Review. 2012. Vol. 27, No. 1. P. 1–31.
  14. Pollack M.E., Ringuette M. Introducing the Tileworld: Experimentally evaluating agent architectures // AAAI. 1990. Vol. 90. P. 183–189.
  15. Schwartz H.M. Multi-agent machine learning: A reinforcement approach. NY: John Wiley & Sons, 2014. 264 p.
  16. Sutton R.S., Barto A.G. Reinforcement learning: An introduction. Cambridge: MIT press, 2018. 548 p.
  17. Tan M. Multi-agent reinforcement learning: Independent vs. cooperative agents // Proceedings of the tenth international conference on machine learning. 1993. P. 330–337.
  18. Tesauro G. Extending Q-learning to general adaptive multiagent systems // Advances in neural information processing systems. 2003. Vol. 16. P. 871–878.
  19. Tuyls K., Weiss G. Multiagent learning: Basics, challenges, and prospects // AI Magazine. 2012. Vol. 33. No. 3. P. 41–41.
  20. Verma T., Varakantham P., Lau H.C. Entropy based independent learning in anonymous multi-agent settings // Proceedings of the International Conference on Automated Planning and Scheduling. 2019. Vol. 29. No. 1. P. 655–663.
  21. Whiteson S. Learning with Opponent — Learning Awareness // Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems. 2018.
  22. Zhao Y. et al. Winning Isn’t Everything: Enhancing Game Development with Intelligent Agents // IEEE Transactions on Games. 2020. Vol. 12. No. 2. P. 199—212.
  23. Zinkevich M. Online convex programming and generalized infinitesimal gradient ascent // Proceedings of the 20th international conference on machine learning. 2003. P. 928–936.
  24. Abouheaf M. Optimization and reinforcement learning techniques in multiagent graphical games and economic dispatch: PhD thesis. The University of Texas at Arlington, 2012. 213 p.
  25. Beck-Courcelle D. et al. Study of Multiple Multiagent Reinforcement Learning Algorithms in Grid Games: MASc thesis. Carleton University, 2013. 109 p.
  26. Berger U. Brown’s original fictitious play // Journal of Economic Theory. 2007. Vol. 135. No. 1. P. 9.
  27. Bowling M. Multiagent learning in the presence of agents with limitations: PhD thesis. Carnegie Mellon University, 2003. P. 172.
  28. Bowling M., Veloso M. Multiagent learning using a variable learning rate // Artificial Intelligence. 2002. Vol. 136. No. 2. P. 215–250.
  29. Bowling M., Veloso M. Rational and Convergent Learning in Stochastic Games // International joint conference on artificial intelligence. 2001. Vol. 17. No. 1. P. 1021–1026.
  30. Brau B.C. An Exploration of Multiagent Learning Within the Game of Sheephead: MSc thesis. Minnesota: State University, 2011. 79 p.
  31. Busoniu L. et al. Multiagent reinforcement learning: A survey // 9th International Conference on Control, Automation, Robotics and Vision. 2006. P. 7.
  32. Busoniu L. et al. Multiagent Reinforcement Learning: An Overview // Innovations in MultiAgent Systems and Applications. 2010. No. 1. P. 183–221.
  33. Casgrain P. et al. Deep Q-Learning for Nash Equilibria: Nash-DQN. URL: https://arxiv.org/abs/1904.10554 (дата обращения 01.11.2020).
  34. Claus C., Boutilier C. The dynamics of reinforcement learning in cooperative multiagent systems // AAAI/IAAI. 1998. Vol. 1. P. 7
  35. Conitzer V., Sandholm T. AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents // Machine Learning. 2007. No. 67. P. 23–43.
  36. Hernandez-Leal P. et al. A survey of learning in multiagent environments: Dealing with non-stationarity. URL: https://arxiv.org/abs/1707.09183. 2019 (дата обращения 01.11.2020).
  37. Hu J., Wellman M.P. Multiagent reinforcement learning: Theoretical framework and an algorithm // ICML. 1998. Vol. 98. P. 242–250.
  38. Hu J., Wellman M.P. Nash Q-Learning for General-Sum Stochastic Games // Journal of machine learning research. 2003. No. 4. P. 1039–1069.
  39. Kapetanakis S., Kudenko D. Reinforcement Learning of Coordination in Cooperative Multiagent Systems // AAAI/IAAI. 2002. Vol. 1. P. 6.
  40. Lemke C.E., Howson J.T. Equilibrium Points of Bimatrix Games // Journal of the Society for Industrial and Applied Mathematics. 1964. Vol. 12. No. 2. P. 413–423.
  41. Littman M.L. Markov games as a framework for multiagent reinforcement learning // Machine learning proceedings. 1994. P. 157–163.
  42. Littman M.L., Stone P. Implicit Negotiation in Repeated Games // International Workshop on Agent Theories, Architectures, and Languages. 2001. P. 393–404.
  43. Liu T. et al. Game Theoretic Control of Multiagent Systems // SIAM Journal on Control and Optimization. 2019. Vol. 57. No. 3. P. 1691–1709.
  44. Lu X. MultiAgent Reinforcement Learning in Games: PhD thesis. Carleton University, 2012. 188 p.
  45. Nowé A. et al. Game Theory and Multiagent Reinforcement Learning // Reinforcement Learning. 2012. P. 441–470.
  46. Park Y.J. et al. Multiagent reinforcement learning with approximate model learning for competitive games // PLoS ONE. 2019. Vol. 14. No. 9. P. 21.
  47. Powers R., Shoham Y. New Criteria and a New Algorithm for Learning in MultiAgent Systems // Advances in Neural Information Processing Systems. 2004. P. 8.
  48. Qiao H. Multiagent learning with bargaining — a game theoretic approach: PhD thesis. University of Arizona, 2007. P. 112.
  49. Roberson B. The Colonel Blotto game // Economic Theory. Vol. 29. No. 1. 2006. P. 24.
  50. Rokhlin D.B. Q-learning in a stochastic stackelberg game between an uninformed leader and a naïve follower // Theory Probability Application. 2019. Vol. 64. No. 1. P. 41–58
  51. Roughgarden T. et al. Algorithmic game theory // Communications of the ACM. 2010. Vol. 53. No. 7. P. 775
  52. Schwartz H. MultiAgent Machine Learning: A Reinforcement Approach. NY: John Wiley & Sons, 2014. 264 p.
  53. Sheppard J.W. Multiagent reinforcement learning in markov games: PhD thesis. The Johns Hopkins University, 1998. 268 p.
  54. Shwartz A., Makowski M.A. Comparing Policies in Markov Decision Processes: Mandl’s Lemma Revisited // Mathematics of Operations Research. 1990. Vol. 15. No. 1. P. 155–174.
  55. Singh S.P. et al. Nash Convergence of Gradient Dynamics in General-Sum Games // Uncertainty in artificial intelligence proceedings. 2000. P. 541—548.
  56. Tadelis S. Game Theory An Introduction. Princeton: Princeton University Press, 2013. 416 p.
  57. Tuyls K., Weiss G. Multiagent Learning: Basics, Challenges, and Prospects // AI Magazine. 2012. Vol. 33. No. 3. P. 41–52
  58. Uther W., Veloso M. Adversarial Reinforcement Learning // Proceedings of the AAAI Fall Symposium on Model Directed Autonomous Systems. 1997. P. 22.
  59. Wei E. Learning to play cooperative games via reinforcement learning: PhD thesis. George Mason University. 2018. 164 p.
  60. Wheeler Jr., R. Narendra K. Decentralized Learning in Finite Markov Chains // IEEE Transactions on Automatic Control. 1986. Vol. 31. No. 6. P. 519–526
  61. Xu D. An integrated simulation, learning and game-theoretic framework for supply chain competition: PhD thesis. The University of Arizona, 2014. P. 233.
  62. Yang Z. et al. A theoretical analysis of deep Q-learning. URL: https:// arxiv.org/abs/1901.00137v3 (дата обращения 01.11.2020).
  63. Baker B., Kanitscheider I., Markov T., Wu Y. Emergent Tool Use From Multi-Agent Autocurricula. URL: https://arxiv.org/abs/1909.07528 (дата обращения 01.11.2020).
  64. Cassandra A. A Survey of POMDP Applications // AAAI. 1998. Vol. 1724. P. 1–9.
  65. Foerster J. Deep multi-agent reinforcement learning DeepMARL: PhD thesis. University of Oxford. 2018. URL: https://ora.ox.ac.uk/objects/uuid:a55621b3-53c0-4e1b-ad1c-92438b57ffa4 (дата обращения 01.11.2020).
  66. Foerster J., Assael I., Freitas N., Whiteson S. Learning to Communicate with Deep Multi-Agent Reinforcement Learning // NIPS. 2017. Vol. 29. P. 1—13.
  67. Foerster J., Chen R., Al-Shedivat M., Whiteson S. Learning with Opponent-Learning Awareness. URL: https://arxiv.org/abs/1709.04326 (дата обращения 01.11.2020).
  68. Foerster J., Farquhar G.‚ Afouras T.‚ Nardelli N. Counterfactual multiagent policy gradients. URL: https://arxiv.org/abs/1705.08926 (дата обращения 01.11.2020).
  69. Foerster J., Nardelli N., Farquhar G., Afouras T. Stabilising experience replay for deep multiagent reinforcement learning. URL: https://arxiv.org/abs/1702.08887 (дата обращения 01.11.2020)
  70. Gupta J., Egorov M., Kochenderfer M. Cooperative multiagent control using deep reinforcement learning // International Conference on Autonomous Agents and Multiagent Systems. 2017. P. 66–83.
  71. Hausknecht M.J. Cooperation and communication in multiagent deep reinforcement learning: PhD thesis. The University of Texas at Austin, 2016. 169 p.
  72. Hausknecht M., Stone P. Deep Recurrent Q-Learning for Partially Observable MDPs. URL: https://arxiv.org/abs/1507.06527 (дата обращения 01.11.2020).
  73. He H., Boyd-Graber J., Kwok K., Daume H. Opponent Modeling in Deep Reinforcement Learning. URL: https://arxiv.org/abs/1609.05559 (дата обращения 01.11.2020).
  74. Hernandez-Leal P., Kartal B., Taylor M. A survey and critique of multiagent deep reinforcement learning // Autonomous Agents and Multi-Agent Systems. 2019. Vol. 33. P. 750–797.
  75. Hernandez-Leal P., Kartal B., Taylor M. Is multiagent deep reinforcement learning the answer or the question? A brief survey. URL: https://arxiv.org/abs/1810.05587v2 (дата обращения 01.11.2020).
  76. Hessel M., Modayil J., Hasselt H.V., Schaul T. Rainbow: Combining Improvements in Deep Reinforcement Learning. URL: https://arxiv.org/abs/1710.02298 (дата обращения 01.11.2020).
  77. Hong Z., Su S., Shann T., Chang Y. A Deep Policy Inference Q-Network for Multi-Agent Systems. URL: https://arxiv.org/abs/1712.07893 (дата обращения 01.11.2020)
  78. Jaakkola T., Singh S., Jordan M. Reinforcement learning algorithm for partially observable Markov decision problems // NIPS. 1994. Vol. 4. P. 345–352.
  79. Konda V., Tsitsiklis J. Actor-Critic Algorithms // NIPS. 2000. Vol. 13. P. 1008–1014.
  80. Lapan M. Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. Birmingham: Packt Publishing Ltd, 2020. 827 p.
  81. Lazaridou A., Peysakhovich A., Baroni M. Multi-Agent Cooperation and the Emergence of (Natural) Language. URL: https://arxiv.org/abs/1612.07182 (дата обращения 01.11.2020)
  82. Lerer A., Peysakhovich A. Maintaining cooperation in complex social dilemmas using deep reinforcement learning. URL: https://arxiv.org/abs/1707.01068 (дата обращения 01.11.2020).
  83. Lillicrap T., Hunt J., Pritzel A., Heess N. Continuous control with deep reinforcement learning. URL: https://arxiv.org/abs/1509.02971 (дата обращения 01.11.2020).
  84. Liu Y., Wang W., Hu Y., Hao J. Multi-Agent Game Abstraction via Graph Attention Neural Network. URL: https://arxiv.org/abs/1911.10715v1 (дата обра-щения 01.11.2020).
  85. Lowe R., Wu Y., Tamar A., Harb J. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. URL: https://arxiv.org/abs/1706.02275 (дата обращения 01.11.2020).
  86. Matignon L., Laurent G.J., Le Fort-Piat N. Independent reinforcement learners in cooperative Markov games a survey regarding coordination problems // CUP. 2012. Vol. 27. No. 1. P. 1–31.
  87. Mnih V., Kavukcuoglu K., Silver D., Graves A. Playing Atari with Deep Reinforcement Learning. URL: https://arxiv.org/abs/1312.5602 (дата обращения 01.11.2020).
  88. Mnih V., Kavukcuoglu K., Silver D., Rusu A. Human-Level Control Through Deep Reinforcement Learning // Nature. 2015. Vol. 518. P. 529–533.
  89. Oliehoek F., Amato C. A concise introduction to decentralized POMDPs. Berlin: Springer, 2016. 141 p.
  90. Omidshafiei S., Pazis J., Amato C., How J. Deep decentralized multi-task multi-agent reinforcement learning under partial observability. URL: https://arxiv.org/abs/1703.06182 (дата обращения 01.11.2020).
  91. Oroojlooy Jadid A., Hajinezhad D. A Review of Cooperative Multi-Agent Deep Reinforcement Learning. URL: https://arxiv.org/abs/1908.03963 (дата обращения 01.11.2020).
  92. Palmer G., Tuyls K., Bloembergen D., Savani R. Lenient multi-agent deep reinforcement learning. URL: https://arxiv.org/abs/1707.04402 (дата обращения 01.11.2020).
  93. Panait L., Sullivan K., Luke S. Lenient learners in cooperative multiagent systems // Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems. 2006. P. 801–803
  94. Pham H.N.A., Triantaphyllou E. The Impact of Overfitting and Over-generalization on the Classification Accuracy in Data Mining: PhD thesis. Louisiana State University, 2011. 127 p.
  95. Puigdomènech A., Piot B., Kapturowski S., Sprechmann P. Agent57: Outper-forming the Atari Human Benchmark. URL: https://arxiv.org/abs/2003.13350 (дата обращения 01.11.2020).
  96. Rashid T., Samvelyan M., Witt C., Farquhar G. QMIX: Monotonic Value Function Factorisation for DeepMultiAgent Reinforcement Learning. URL: https://arxiv.org/abs/1803.11485 (дата обращения 01.11.2020).
  97. Schmidhuber J., Hochreiter S. Long short-term memory // Neural Pomputation. 1997. Vol. 9. No. 8. P. 1735–1780.
  98. Schrittwieser J., Antonoglou I., Hubert T., Simonyan K. Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. URL: https://arxiv.org/abs/1911.08265 (дата обращения 01.11.2020).
  99. Schulman J., Levine S., Abbeel P., Jordan M. Trust region policy optimization // PMLR. 2015. Vol. 37. P. 1889–1897.
  100. Silver D., Hubert T., Schrittwieser J., Antonoglou I. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play // Science. 2018. Vol. 362. P. 1140–1144.
  101. Silver D., Lever G., Heess N., Degris T. Deterministic policy gradient algorithms // ICML. 2014. Vol. 32. P. 387–395.
  102. Silver D., Schrittwieser J., Simonyan K., Antonoglou I. Mastering the game of Go without human knowledge // Nature. 2017. Vol. 550. P. 354–359.
  103. Son K., Kim D., Kang W.J., Hostallero D.E., Yi Y. QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning. URL: https://arxiv.org/abs/1905.05408 (дата обращения 01.11.2020).
  104. Sukhbaatar S., Szlam A., Fergus R. Learning Multiagent Communication with Backpropagation. URL: https://arxiv.org/abs/1605.07736 (дата обращения 01.11.2020).
  105. Sunehag P., Lever G., Gruslys A., Czarnecki W.M. Value-decomposition networks for cooperative multi-agent learning based on team reward // AAMAS. 2018. Vol. 17. P. 2085–2087.
  106. Tampuu A., Matiisen T., Kodelja D., Kuzovkin I. Multiagent cooperation and competition with deep reinforcement learning // PLoS One. 2017. Vol. 12. No. 4.
  107. Usunier N., Synnaeve G., Lin Z., Chintala S. Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks. URL: https://arxiv.org/abs/1609.02993 (дата обращения 01.11.2020).
  108. 215Sukhbaatar S., Szlam A., Fergus R. Learning Multiagent Communication with Backpropagation. URL: https://arxiv.org/abs/1605.07736 (дата обращения 01.11.2020).Sunehag P., Lever G., Gruslys A., Czarnecki W.M. Value-decomposition networks for cooperative multi-agent learning based on team reward // AAMAS. 2018. Vol. 17. P. 2085–2087.Tampuu A., Matiisen T., Kodelja D., Kuzovkin I. Multiagent cooperation and competition with deep reinforcement learning // PLoS One. 2017. Vol. 12. No. 4.Usunier N., Synnaeve G., Lin Z., Chintala S. Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks. URL: https://arxiv.org/abs/1609.02993 (дата обращения 01.11.2020).
  109. Vinyals O., Babuschkin I., Czarnecki W., Mathieu M. Grandmaster level in StarCraft II using multi-agent reinforcement learning // Nature. 2019. Vol. 575. P. 350–354.
  110. Wang W., Yang T., Liu L., Hao J. From Few to More: Large-Scale Dynamic Multiagent Curriculum Learning. URL: https://arxiv.org/abs/1909.02790v2 (дата обращения 01.11.2020)
  111. Wei E., Luke S. Lenient learning in independent-learner stochastic cooperative games // The Journal of MLR. 2016. Vol. 17. No. 1.
  112. Werbos P.J. Backpropagation through time: what it does and how to do it // IEEE. 1990. Vol. 78. No. 10. P. 1550–1560.
  113. Zheng Y., Meng Z., Hao J., Zhang Z., Yang T. A deep bayesian policy reuse approach against non-stationary agents // NIPS. 2018. Vol. 1. P. 962–972.
  114. Карпенко А.П. Современные алгоритмы поисковой оптимизации. Ал-горитмы, вдохновленные природой: учебное пособие. М.: Изд-во МГТУ им. Н.Э. Баумана, 2017. 446 с.
  115. Almufti S., Marqas R., Ashqi V. Taxonomy of bio-inspired optimization algorithms // Journal of Advanced Computer Science & Technology. 2019. Vol. 8. No. 2. P. 23–31.
  116. Baar W., Bauso D. Networked Bio-Inspired Evolutionary Dynamics on a Multi-Population // 18th European Control Conference. 2019. P. 1023–1028.
  117. De Jong K. Evolutionary computation: a unified approach // Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2020. P. 327–342.
  118. Ficici S.G., Pollack J.B. Pareto optimality in coevolutionary learning // European Conference on Artificial Life. Berlin: Springer, 2001. P. 316–325.
  119. Gill S.S., Buyya R. Bio-inspired algorithms for big data analytics: a survey, taxonomy, and open challenges // Big Data Analytics for Intelligent Healthcare Management. NY: Academic Press, 2019. P. 1–17.
  120. Gomes J., Mariano P., Christensen A.L. Cooperative coevolution of partially heterogeneous multiagent systems // Proceedings of the International Conference on Autonomous Agents and Multiagent Systems. 2015. P. 297–305.
  121. Gomes J., Mariano P., Christensen A.L. Dynamic team heterogeneity in cooperative coevolutionary algorithms // IEEE Transactions on Evolutionary Computation. 2017. Vol. 22. No. 6. P. 934–948
  122. Jacob C. et al. Illustrating evolutionary computation with Mathematica. Voltem: Morgan Kaufmann, 2001. 547 p.
  123. Jaderberg M. et al. Population based training of neural networks. URL: https://arxiv.org/abs/1711.09846 (дата обращения 01.11.2020).
  124. Khalid M.A., Yusof U., Aman K.K. A survey on bio-inspired multi-agent system for versatile manufacturing assembly line // ICIC Express Letters. 2016. Vol. 10. No. 1. P. 1–7.
  125. Knudson M., Tumer K. Coevolution of heterogeneous multi-robot teams // Proceedings of the 12th annual conference on Genetic and evolutionary computation. 2010. P. 127–134.
  126. Leibo J.Z. et al. Malthusian reinforcement learning. URL: https://arxiv.org/abs/1812.07019 (дата обращения 01.11.2020).
  127. Levin S.A., Udovic J.D. A mathematical model of coevolving populations // The American Naturalist. 1977. Vol. 111. No. 980. P. 657–675.
  128. Li Z., Liu J. A multi-agent genetic algorithm for community detection in complex networks // Physica A: Statistical Mechanics and its Applications. 2016. Vol. 449. P. 336–347.
  129. Liekens A.M.L., ten Eikelder H.M.M., Hilbers P.A.J. Finite population models of co-evolution and their application to haploidy versus diploidy // Genetic and Evolutionary Computation Conference. Berlin: Springer, 2003. P. 344–355.
  130. Liu S. et al. Emergent coordination through competition. URL: https:// arxiv.org/abs/1902.07151(дата обращения 01.11.2020).
  131. Miikkulainen R. et al. Multiagent learning through neuroevolution // IEEE World Congress on Computational Intelligence. Berlin: Springer, 2012. P. 24–46.
  132. Moriarty D.E., Miikkulainen R. Forming neural networks through efficient and adaptive coevolution // Evolutionary computation. 1997. Vol. 5. No. 4. P. 373–399
  133. Omidshafiei S. et al. α-rank: Multi-agent evaluation by evolution // Scientific reports. 2019. Vol. 9. No. 1. P. 1–29.
  134. Panait L., Luke S. Cooperative multi-agent learning: The state of the art // Autonomous agents and multi-agent systems. 2005. Vol. 11. No. 3. P. 387–434.
  135. Panait L., Sullivan K., Luke S. Lenience towards teammates helps in cooperative multiagent learning // Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi Agent Systems. 2006. P. 1–10
  136. Paredis J. Coevolutionary computation // Artificial life. 1995. Vol. 2. No. 4. P. 355–375.
  137. Peng Z., Wu J., Chen J. Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm // Journal of Central South University of Technology. 2011. Vol. 18. No. 5. P. 1502
  138. Rockefeller G., Khadka S., Tumer K. Multi-level Fitness Critics for Cooperative Coevolution // Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems. 2020. P. 1143–1151.
  139. Rossi F. et al. Review of multi-agent algorithms for collective behavior: a structural taxonomy // IFAC-PapersOnLine. 2018. Vol. 51. No. 12. P. 112–117.
  140. Schmitt L.M. Theory of coevolutionary genetic algorithms // International Symposium on Parallel and Distributed Processing and Applications. Berlin: Springer, 2003. P. 285–293.
  141. Schmitt L.M. Theory of genetic algorithms // Theoretical Computer Science. 2001. Vol. 259. No. 1–2. P. 1–61
  142. Seredynski F. Coevolutionary multi-agent systems: the application to mapping and scheduling problems // Proceedings of the IEEE International Conference on Industrial Technology. 1996. P. 431–435.
  143. Seredynski F. Competitive coevolutionary multi-agent systems: The application to mapping and scheduling problems // Journal of Parallel and Distributed Computing. 1997. Vol. 47. No. 1. P. 39–57.
  144. Singh S. et al. Intrinsically motivated reinforcement learning: An evolutionary perspective // IEEE Transactions on Autonomous Mental Development. 2010. Vol. 2. No. 2. P. 70–82.
  145. Singh S., Lewis R.L., Barto A.G. Where do rewards come from // Proceedings of the annual conference of the cognitive science society. 2009. P. 2601–2606
  146. Srivastava V., Leonard N.E. Bio-inspired decision-making and control: From honeybees and neurons to network design // American Control Conference. 2017. P. 2026–2039.
  147. Stella L. Bio-Inspired Collective Decision-Making in Game Theoretic Models and Multi-Agent Systems: PhD thesis. University of Sheffield, 2019. 141 p.
  148. Stone P., Veloso M. Multiagent systems: A survey from a machine learning perspective // Autonomous Robots. 2000. Vol. 8. No. 3. P. 345–383
  149. Uriot T., Izzo D. Safe Crossover of Neural Networks Through Neuron Alignment. URL: https://arxiv.org/abs/2003.10306 (дата обращения 01.11.2020).
  150. Wang Y., Qi Y., Li Y. Memory-based multiagent coevolution modeling for robust moving object tracking // The Scientific World Journal. 2013. Vol. 2013. P. 1—13.
  151. Whiteson S., Stone P. Evolutionary function approximation for reinforcement learning // Journal of Machine Learning Research. 2006. Vol. 7. P. 877–917.
  152. Wiegand R.P., Liles W.C., De Jong K.A. Modeling Variation in Cooperative Coevolution Using Evolutionary Game Theory // 7th International Workshop Foundations of Genetic Algorithms. 2002. P. 203–220.
  153. Yang Z., Tang K., Yao X. Large scale evolutionary optimization using cooperative coevolution // Information sciences. 2008. Vol. 178. No. 15. P. 2985–2999.
  154. Yong C.H., Miikkulainen R. Coevolution of role-based cooperation in multiagent systems // IEEE Transactions on Autonomous Mental Development. 2009. Vol. 1. No. 3. P. 170–186.
  155. Aubret A., Matignon L., Hassas S. A survey on intrinsic motivation in reinforcement learning. URL: https://arxiv.org/abs/1908.06976(дата обращения 01.11.2020).
  156. Bellemare M. et al. Unifying count-based exploration and intrinsic motivation // NIPS. 2016. Vol. 29. P. 1471–1479.
  157. Chakraborty A., Kar A.K. Swarm intelligence: A review of algorithms // Nature-Inspired Computing and Optimization. Berlin: Springer, 2017. P. 475–494.
  158. Coppola M. et al. A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints // Frontiers in Robotics and AI. 2020. Vol. 7. P. 18.
  159. Dorigo M., Blum C. Ant colony optimization theory: A survey // Theoretical computer science. 2005. Vol. 344. No. 2–3. P. 243–278.
  160. Dorigo M., Stützle T. Ant colony optimization: overview and recent advances // Handbook of metaheuristics. Berlin: Springer, 2019. P. 311–351
  161. Gambardella L.M., Dorigo M. Ant-Q: A reinforcement learning approach to the traveling salesman problem // Machine Learning Proceedings. 1995. P. 252–260.
  162. GhasemAghaei R. et al. Ant colony-based reinforcement learning algorithm for routing in wireless sensor networks // Instrumentation & Measurement Technology Conference. 2007. P. 1–6.
  163. Hüttenrauch M. et al. Deep reinforcement learning for swarm systems // Journal of Machine Learning Research. 2019. Vol. 20. No. 54. P. 1–31.
  164. Hüttenrauch M., Šošić A., Neumann G. Local communication protocols for learning complex swarm behaviors with deep reinforcement learning // International Conference on Swarm Intelligence. Berlin: Springer, 2018. P. 71–83.
  165. Khan A. et al. Collaborative multiagent reinforcement learning in homogeneous swarms. URL: https://openreview.net/pdf?id=ByeDojRcYQ (дата обращения 01.11.2020).
  166. Kho L.C. et al. Ant colony optimization for 2 satisfiability in restricted neural symbolic integration // AIP Conference Proceedings. 2020. Vol. 2266. No. 1. P. 050006.
  167. Matta M. et al. Q-RTS: a real-time swarm intelligence based on multi-agent Q-learning // Electronics Letters. 2019. Vol. 55. No. 10. P. 589–591.
  168. Oh K.K., Park M.C., Ahn H.S. A survey of multi-agent formation control // Automatica. 2015. Vol. 53. P. 424–440.
  169. Ostrovski G. et al. Count-based exploration with neural density models. URL: https://arxiv.org/abs/1703.01310 (дата обращения 01.11.2020).
  170. Schranz M. et al. Swarm Robotic Behaviors and Current Applications // Frontiers in Robotics and AI. 2020. Vol. 7. P. 36.
  171. Singh P. et al. Swarm Intelligence Algorithms: A Tutorial. Boca Raton: CRC press, 2020. 363 p.
  172. Socha K., Blum C. An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training // Neural Computing and Applications. 2007. Vol. 16. No. 3. P. 235–247.
  173. Šošic A. et al. Inverse reinforcement learning in swarm systems // Proceedings of the 1st Workshop on Transferin Reinforcement Learning at the 16th International Conference on Autonomous Agents and Multiagent Systems. Sao Paulo, Brazil, 2017. P. 17.
  174. Rizk Y., Awad M., Tunstel E.W. Decision making in multiagent systems: A survey // IEEE Transactions on Cognitive and Developmental Systems. 2018. Vol. 10. No. 3. P. 514–529.
  175. Tarassov V.B., Gapanyuk Y.E. Complex Graphs in the Modeling of Multi-agent Systems: From Goal-Resource Networks to Fuzzy Metagraphs // Russian Conference on Artificial Intelligence. Berlin: Springer, 2020. P. 177–198.
  176. Yang X.S. (ed.). Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications. NY: Academic Press, 2020.
  177. Zhou S., Yan S. The stability analysis for a class of multi-agent group formation with input saturation constraints // Proceedings of Chinese Guidance, Navigation and Control Conference. 2014. P. 1618–1623.
Ваш браузер устарел и не обеспечивает полноценную и безопасную работу с сайтом.
Установите актуальную версию вашего браузера или одну из современных альтернатив.