Boudjemaa Khelifa
ABSTRACT
Actually, planners always need more and more updated plans to satisfy eventual changes. Those plans usually bring immediate and even sustainable solutions. According to the nature of problems, and decision-makers yearnings, the allocated time to establish plans is still tight. Therefore, adequate technics and methods are suitable to tackle this problem. Recently, a primer research field called Machine learning (ML), whose technics are based on learning by studying data or by applying known rules to categorize things, to predict outcomes, to identify patterns, or to detect unexpected behaviors. Reinforcement learning (RL) is an active research field of ML, based on learning how to map situations to actions, so as to maximize a numerical reward. By employing RL methods, the aim is to provide better plans for urban projects, wherein they are modeled to form a multi-agents system, acting cooperatively and optimally.
- Russell Stuart and Norvig Peter. 2010. Artificial Intelligence: A Modern Approach. Third Edit. Prentice Hall, Upper Saddle River, (2010). Google Scholar
Digital Library
- Sugiyama Masashi. 2015. Statistical reinforcement learning: modern machine learning approaches. Chapman and Hall/CRC, (2015). Google ScholarDigital Library
- Sutton Richard S. and Barto Andrew G. 2018. Reinforcement learning: An introduction. MIT press, 2018. Google ScholarDigital Library
- Szepesvári, Csaba. 2010. Algorithms for reinforcement learning. Synthesis lectures on artificial intelligence and machine learning, 4, 1, (2010), 1--103.Google Scholar
- Buşoniu Lucian, Babuška Robert, and De Schutter Bart. 2010. Multi-agent reinforcement learning: An overview. In: Innovations in multi-agent systems and applications-1. Springer, Berlin, Heidelberg, (2010), 183--221.Google Scholar
- Vidhate Deepak A. and Kulkarni Parag. 2016. Innovative approach towards cooperation models for multi-agent reinforcement learning (CMMARL). In: International Conference on Smart Trends for Information Technology and Computer Communications. Springer, Singapore, (2016), 468--478.Google ScholarCross Ref
- Raileanu Roberta, Denton Emily, Szlam Arthur, et al. 2018. Modeling others using oneself in multi-agent reinforcement learning. arXiv preprint arXiv:1802.09640, (2018).Google Scholar
- Khelifa Boudjemaa and Laouar Mohamed Ridda. 2018. Urban projects planning by multi-objective ant colony optimization algorithm. In: Proceedings of the 8th International Conference on Information Systems and Technologies, ICIST '18, ACM, (2018).Google Scholar
- Khelifa Boudjemaa, Laouar Mohamed Ridda, and Eom Sean. 2018. Towards an Intelligent Integrated System for Urban Planning Using GIS and Cloud Computing. In: International Conference on Decision Support System Technology. Springer, Cham, (2018), 26--37.Google Scholar
- Gwo-Hshiung Tzeng, and Jih-Jeng Huang. 2013. Fuzzy multiple objective decision making. CRC Press, (2013).Google ScholarCross Ref
- Zolfpour-Arokhlo Mortaza, Selamat Ali, Hashim Siti Zaiton Mohd, et al. 2014. Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms. Engineering Applications of Artificial Intelligence, 29, (2014), 163--177. Google ScholarDigital Library
- Vidhate Deepak A. and Kulkarni Parag. 2016. Innovative approach towards cooperation models for multi-agent reinforcement learning (CMMARL). In: International Conference on Smart Trends for Information Technology and Computer Communications. Springer, Singapore, (2016), 468--478.Google ScholarCross Ref
- Mannion Patrick, Mason Karl, Devlin Sam, et al. 2016. Multi-objective dynamic dispatch optimization using multi-agent reinforcement learning. In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems, (2016), 1345--1346. Google ScholarDigital Library
- Perolat Julien, Leibo Joel Z., Zambaldi Vinicius, et al. 2017. A multi-agent reinforcement learning model of common-pool resource appropriation. In: Advances in Neural Information Processing Systems, (2017), 3643--3652. Google ScholarDigital Library
- Aydin Mehmet Emin and Fellows Ryan. 2017. A reinforcement learning algorithm for building collaboration in multi-agent systems. arXiv preprint arXiv:1711.10574, (2017).Google Scholar
- Zhang Zhen, Wang Dongqing, Zhao, Dongbin, et al. 2017. FMR-GA--A Cooperative Multi-agent Reinforcement Learning Algorithm Based on Gradient Ascent. In: International Conference on Neural Information Processing. Springer, Cham, (2017), 840--848.Google Scholar
- Yang Jiachen, Nakhaei Alireza, Isele David, et al. 2018. CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning. arXiv preprint arXiv:1809.05188, (2018).Google Scholar
Index Terms
- Multi-agent Reinforcement Learning for Urban Projects Planning
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