یک چارچوب یادگیری تقویتی عمیق عامل-منتقد برای تصمیم‌گیری متوالی چند هدفه

[1] N. Gunantara, “A review of multi-objective optimization: Methods and its applications”, Cogent Engineering, vol. 5, no. 1, pp. 1–16, 2018.

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[3] Q. Q. Wang, Z. D. Li, W. W. Wang, C. L. Zhang, L. Q. Chen, and L. Wan, “Multi-objective optimization design of wheat centralized seed feeding device based on particle swarm optimization (PSO) algorithm”, International Journal of Agricultural and Biological Engineering, vol. 13, no. 6, pp. 76–84, 2020.

[4] B. Razaghi, M. Roayaei, and N. M. Charkari, “On the Group-Fairness-Aware Influence Maximization in Social Networks”, IEEE Transactions on Computational Social Systems, 2022.

[5] M. A. Wiering, M. Withagen, and M. M. Drugan, “Model-based multi-objective reinforcement learning”, IEEE symposium on adaptive dynamic programming and reinforcement learning, pp. 1-6, 2014.

[6] R. Yang, X. Sun, and K. Narasimhan, “A generalized algorithm for multi-objective reinforcement learning and policy adaptation”  Advances in Neural Information Processing Systems, vol. 32, 2019.

[7] J. Xu, Y. Tian, P. Ma, D. Rus, S. Sueda, and W. Matusik, “Prediction-guided multi-objective reinforcement learning for continuous robot control”, 37th International Conference on Machine Learning (ICML),   pp. 10538–10547, 2020.

[8] D. M. Roijers, P. Vamplew, S. Whiteson, and R. Dazeley, “A Survey of Multi-Objective Sequential Decision-Making”, Journal of Artificial Intelligence Research, vol. 48, pp. 67–113, 2013.

[9] M. Rodriguez, C. Posse, and E. Zhang, “Multiple objective optimization in recommender systems”, Proceedings of the sixth ACM conference on Recommender systems, pp. 11-18. 2012.

[10] C. Li and K. Czarnecki, “Urban Driving with Multi-Objective Deep Reinforcement Learning”, Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pp. 359-367, 2019.

[11] Z. Miao, J. Yu, J. Ji, and J. Zhou, “Multi-objective region reaching control for a swarm of robots”. Automatica, vol. 103, pp. 81–87, 2019.

[12] J. Jin and X. Ma, “A Multi-Objective Agent-Based Control Approach with Application in Intelligent Traffic Signal System”, IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, pp. 3900–3912, 2019.

[13] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. Riedmiller, “Playing Atari with Deep Reinforcement Learning”, arXiv preprint arXiv:1312.5602, 2013.

[14] T. T. Nguyen, N. D. Nguyen, P. Vamplew, S. Nahavandi, R. Dazeley, C. P. Lim, “A multi-objective deep reinforcement learning framework”,  Engineering Applications of Artificial Intelligence, vol. 96, p. 103915, 2020.

[15] N. M. Tabatabaei, S.R. Mortezaei, S. Shargh, & B. Khorshid,  "Solving multi-objective optimal power flow using multi-objective electro search algorithm." International Journal on Technical and Physical Problems of Engineering, 9, pp. 1-8, 2017.

[16] N. Tabrizi, E. Babaei, & M. Mehdinejad, M.  “An interactive fuzzy satisfying method based on particle swarm optimization for multi-objective function in reactive power market”, Iranian Journal of Electrical and Electronic Engineering, 12(1), 65-72, 2016.

[17] W. Liu, F. Liu, R. Tang, B. Liao, G. Chen, and P. A. Heng, “Balancing Between Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12084, pp. 155–167, 2020.

[18] M. A. Wiering, E. D. De Jong, “Computing optimal stationary policies for multi-objective Markov decision processes”, Proceedings of the 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning, pp. 158–165, 2007.

[19] M. Khamis, W. Gomaa, “Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework”, Engineering Applications of Artificial Intelligence, vol. 29, pp. 134-151, 2014.

[20] J. Lu, H. Liu, Q. Chen, G. Chen, “MoTiAC : Multi-objective Actor-Critics for Real-time Bidding in Display Advertising”, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 20-37, 2022.

[21] M. Reymond, C. Hayes, D. M. Roijers, D. Steckelmacher, A. Nowé, “Actor-Critic Multi-Objective Reinforcement Learning for Non-Linear Utility Functions”, Autonomous Agents and Multi-Agent Systems, vol. 37, no. 2, 2023.

[22] N. D. Nguyen, T. T. Nguyen, P. Vamplew, R. Dazeley, S. Nahavandi, “A Prioritized objective actor-critic method for deep reinforcement learning”, Neural Computing and Applications, vol. 33, no. 16, pp. 10335–10349, 2021.

[23] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I Polosukhin, “Attention is all you need”, Advances in neural information processing systems, vol. 30, 2017.

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[25] V. Gajjala, R. Gajjala, A. Birzescu, S. Anarbaeva, “Microfinance in online space: a visual analysis of kiva.org”, Development in Practice, vol. 21, no. 6, pp. 880–893, 2011.

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[27] T. Mahmood, F. Ricci, “Learning and adaptivity in interactive recommender systems”, ACM International Conference Proceeding Series, vol. 258, pp. 75–84, 2007.