Abstract
Natural disasters are devastating for a country and effective allocation of critical resources can mitigate the impact. While traditional approaches usually have difficulties in making optimal critical resource allocation, in this paper we introduce a novel hierarchical multi-agent reinforcement learning framework to model optimal resource allocation for natural disasters in real-time. On the lower level a set of agents navigate with the continuous time environment using deep reinforcement algorithms. On the higher level, a lead agent takes care of the global decision-making. Our framework achieves more efficient resource allocation in response to dynamic events and is applicable to problems where disaster evolves alongside the response efforts, where delays in response can lead to increased disaster severity and thus a greater need for resources.
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References
United Nations Office for Disaster Risk Reduction (UNISDR) and Centre for Research on the Epidemiology of Disasters (CRED), Economic losses, poverty and disasters, pp. 1998–2017 (2018)
Frias-Martinez, E., Williamson, G., Frias-Martinez, V.: An agent-based model of epidemic spread using human mobility and social network information. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, pp. 57–64. IEEE (2011)
Wesolowski, A., et al.: Quantifying the impact of human mobility on malaria. Science 338(6104), 267–270 (2012)
Bengtsson, L., Lu, X., Thorson, A., Garfield, R., Von Schreeb, J.: Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS Med. 8(8), e1001083 (2011)
Abbasi, M.-A., Kumar, S., Filho, J.A.A., Liu, H.: Lessons learned in using social media for disaster relief - ASU Crisis Response Game. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds.) SBP 2012. LNCS, vol. 7227, pp. 282–289. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29047-3_34
Chandan, S., et al.: Modeling the interaction between emergency communications and behavior in the aftermath of a disaster. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 476–485. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37210-0_52
Petrovic, N., Alderson, D.L., Carlson, J.M.: Dynamic resource allocation in disaster response: tradeoffs in wildfire suppression. PloS one 7(4), e33285 (2012)
Estuar, M.R.J.E., Rodrigueza, R.C., Victorino, J.N.C., Sevilla, M.C.V., De Leon, M.M., Rosales, J.C.S.: Agent-Based modeling approach in understanding behavior during disasters: measuring response and rescue in eBayanihan disaster management platform. In: Lee, D., Lin, Y.-R., Osgood, N., Thomson, R. (eds.) SBP-BRiMS 2017. LNCS, vol. 10354, pp. 46–52. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60240-0_5
Albrecht, S.V., Stone, P.: Autonomous agents modelling other agents: a comprehensive survey and open problems. Artif. Intell. 258, 66–95 (2018)
Yang, F., Liu, B., Dong, W.: Optimal control of complex systems through variational inference with a discrete event decision process. In: Proceedings of the 2019 International Conference on Autonomous Agents and Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems (2019)
Yang, F., Dong, W.: Integrating simulation and signal processing in tracking complex social systems. Comput. Math. Organ. Theor. 1–22 (2018). Special Issue: SBP-BRIMS2017
Yang, F., Dong, W.: Integrating simulation and signal processing with stochastic social kinetic model. In: Lee, D., Lin, Y.-R., Osgood, N., Thomson, R. (eds.) SBP-BRiMS 2017. LNCS, vol. 10354, pp. 193–203. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60240-0_23
Peng, P., et al.: Multiagent bidirectionally-coordinated nets for learning to play starcraft combat games, arXiv preprint arXiv:1703.10069 (2017)
Matignon, L., Jeanpierre, L., Mouaddib, A.-I.: Coordinated multi-robot exploration under communication constraints using decentralized Markov decision processes. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, pp. 2017–2023. AAAI Press (2012)
Ye, H., Li, G.Y., Juang, B.-H.F.: Deep reinforcement learning based resource allocation for V2V communications, arXiv preprint arXiv:1805.07222 (2018)
Mirzaei, H., Sharon, G., Boyles, S., Givargis, T., Stone, P.: Enhanced delta-tolling: traffic optimization via policy gradient reinforcement learning. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 47–52. IEEE (2018)
Albrecht, S.V., Ramamoorthy, S.: A game-theoretic model and best-response learning method for ad hoc coordination in multiagent systems. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, International Foundation for Autonomous Agents and Multiagent Systems, pp. 1155–1156 (2013)
Peters, J., Schaal, S.: Policy gradient methods for robotics. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2219–2225. IEEE (2006)
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms, arXiv preprint arXiv:1707.06347 (2017)
Acknowledgements
We would like to thank Nathan Margaglio for insightful discussions of the resource allocation problem and SBP-BRiMS reviewers for feedback that has improved this work.
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Vereshchaka, A., Dong, W. (2019). Dynamic Resource Allocation During Natural Disasters Using Multi-agent Environment. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2019. Lecture Notes in Computer Science(), vol 11549. Springer, Cham. https://doi.org/10.1007/978-3-030-21741-9_13
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DOI: https://doi.org/10.1007/978-3-030-21741-9_13
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