بهبود سرعت آموزش در مسائل یادگیری تقویتی مبتنی بر انتقال دانش عصبی‌فازی

[1]      W.Böhmer, J. T.Springenberg, J.Boedecker, M.Riedmiller and K.Obermayer, Autonomous learning of state representations for control: An emerging field aims to autonomously learn state representations for reinforcement learning agents from their real-world sensor observations, KI-Künstliche Intelligenz, vol. 29, no. 4, pp. 353-362, 2015.

[2]      J. Kober, J. A. Bagnell and J. Peters, Reinforcement learning in robotics: A survey, International Journal of Robotics Research, vol. 32, no. 11, pp. 1238-1274, 2013.

[3]      T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y.Tassa and D.Wierstra, Continuous control with deep reinforcement learning,arXiv preprint arXiv:1509.02971, 2015.

 

[4]      عادل اکبری مجد، حسین شایقی، حمید محمد نژاد، عبدااله یونسی، کنترل­کننده مقاوم تطبیقی بار فرکانس مبتنی بر یادگیری تقویتی برای یک سیستم قدرت به هم پیوسته شاملSMES، مجله مهندسی برق دانشگاه تبریز، جلد 47، شماره 2، 1396.

[5]      Y. J. Liu, L. Tang, S. Tong, C. P. Chen and D. J. Li, Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no.1, pp. 165-176, 2015.

[6]      H. B.Ammar, E., Eaton, J. M., Luna and P.Ruvolo, Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning, International Joint Conference on Artificial Intelligence, pp. 3345-3351, 2015.

[7]      A. Fachantidis, I. Partalas, G. Tsoumakas and I. Vlahavas, Transferring task models in reinforcement learning agents, Neurocomputing, vol. 107, pp. 23-32, 2013.

[8]      M.Ghavamzadeh S.Mannor, J.Pineau and A. Tamar, Bayesian reinforcement learning: A survey, Foundations and Trends^® in Machine Learning, vol. 8, no. 5-6, pp. 359-483, 2015.

[9]      A. Gupta, C. Devin, Y. Liu, P. Abbeel and S. Levine, Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning, arXiv preprint arXiv: 1703.02949, 2017.

[10]      O. Mohammed, G.Bailly and D.Pellier, Acquiring Human-Robot Interaction skills with Transfer Learning Techniques, Proceedings of the Companion on Human-Robot Interaction, pp. 359-360, 2017.

[11]      F. L.da Silva and A. H. R. Costa, Accelerating Multiagent Reinforcement Learning through Transfer Learning, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 5034-5035, 2017.

[12]      M. N. Ahmadabadi and M. Asadpour, Expertness based cooperative Q-learning, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 32, no. 1, pp. 66-76, 2002.

[13]      K. Ito, A. Gofuku, Y. Imoto, and M. Takeshita, A study of reinforcement learning with knowledge sharing for distributed autonomous system,Proceedings of the International Symposium on Computational Intelligence in Robotics and Automation, pp. 16-20, 2003.

[14]      R. A. Bianchi, L. A. Celiberto, P. E. Santos, J. P.Matsuura and R. L. deMantaras, Transferring knowledge as heuristics in reinforcement learning: A case-based approach, Artificial Intelligence, vol. 226, pp.102-121, 2015.

[15]      Y. Hou, Y. S.Ong, L. Feng and J. M. Zurada, An Evolutionary Transfer Reinforcement Learning Framework for Multi-Agent System, IEEE Transactions on Evolutionary Computation, vol. 21, no. 4, pp. 601-615, 2017.

[16]      Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever and P. Abbeel, RL²: Fast Reinforcement Learning via Slow Reinforcement Learning. arXiv preprint arXiv:1611.02779, 2016.

[17]      P. Tommasino, D. Caligiore, M. Mirolli and G. Baldassarre, A Reinforcement Learning Architecture that Transfers Knowledge between Skills when Solving Multiple Tasks, IEEETransactions on Cognitive and Developmental Systems, 2016.

[18]      G. F. Wang, Z. Fang, P. Li and B. Li, Transferring knowledge from human-demonstration trajectories to reinforcement learning, Transactions of the Institute of Measurement and Control, vol. 40, no.1, pp. 94-101, 2018.

[19]      R. Glatt, F. L. da Silva and A. H. R. Costa, Towards Knowledge Transfer in Deep Reinforcement Learning, 5^th Brazilian Conference on Intelligent Systems, pp. 91-96, 2016.

[20]      L. Zhou, P. Yang, C. Chen, Y. Gao, Multiagent reinforcement learning with sparse interactions by negotiation and knowledge transfer, IEEE transactions on cybernetics, vol. 47, no. 5, pp. 1238-1250, 2017.

[21]      T. Takano, H. Takase, H. Kawanaka and S. Tsuruoka, Preferential exploration method of transfer learning for reinforcement learning in same transition model, 6^th International Conference on Soft Computing and Intelligent Systems and 13^th International Symposium on Advanced Intelligent Systems, pp. 2099-2103, 2012.

[22]      G. Konidaris, I. Scheidwasser and A. Barto, Transfer in reinforcement learning via shared features, Journal of Machine Learning Research, pp. 1331-1371, 2012.

[23]      B. Banerjee and P. Stone, General Game Learning Using Knowledge Transfer. IJCAI, pp. 672-677. 2007.

[24]      E. Ferrante, A. Lazaric, and M. Restelli, Transfer of task representation in reinforcement learning using policy-based proto-value functions, Proceedings of the 7^th international joint conference on Autonomous agents and multiagent systems, Vol. 3, pp. 1329-1332, 2008.

[25]      A. Lazaric, Knowledge transfer in reinforcement learning, PhD thesis, Politecnico di Milano, 2008.

[26]      سیده ملیحه اخلاقی هاشمی‌پور، انتقال دانش در مسائل یادگیری تقویتی با ویژگی‌های مشترک، پایان نامه کارشناسی ارشد، دانشکده برق و کامپیوتر، دانشگاه یزد، 1394.

 

[27]      R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, Cambridge, MIT Press, 1998.

[28]      G. Yen and T. Hickey, Reinforcement learning algorithms for robotic navigation in dynamic environment, ISI Transaction, vol. 43,no. 2, pp. 217-230, 2004.

[29]      A. Epshteyn and G. Dejong, Qualitative Reinforcement Learning, Proceedings of the 23^rd International Conference on Machine Learning, Pittsburgh, 2006.

[30]      J. Asmuth, M. L. Littman and R. Zinkov, Potential-based shaping in model based reinforcement learning, Proceedings of the 23^rd AAAI conference on Artificial intelligence, pp. 604-609, 2008.

[31]      H. Van Hasselt, A. Guez and D. Silver, Deep Reinforcement Learning with Double Q-Learning, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 1-7, 2016.

  [32]      Q. Wang, L. Ruan and L. Si, Adaptive Knowledge Transfer for Multiple Instance Learning in Image Classification, Proceedings of the 28^rd AAAI Conference on Artificial Intelligence, pp. 1334-1340, 2014.

 

   [33]      حسین مرادی فراهانی، جواد عسگری، طراحی کنترل­کننده عصبی- فازی نوع-2، مجله مهندسی برق دانشگاه تبریز، جلد 43، شماره 1، 1392.

[34]      A. Belaout, F. Krim, A. Mellit, B. Talbi and A. Arabi, Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification, Renewable Energy, vol. 127, pp. 548-558, 2018.

[35]      A. Z. Kamil, S. Rustamov, M. A. Clements and E. Mustafayev, Adaptive Neuro-Fuzzy Inference System for Classification of Texts, Recent Developments and the New Direction in Soft-Computing Foundations and Applications, pp. 63-70, 2018.

[36]      S. V. R. Termeh, A. Kornejady, H. R. Pourghasemi and S. Keesstra, Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms, Science of the Total Environment, vol. 615, pp. 438-451, 2018.

[37]      D. Wang, T. He, Z. Li, L. Cao, N. Dey, A. S. Ashour, ... and F. Shi, Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system, Neural Computing and Applications, vol. 29, no. 4, pp. 1087-1102, 2018.