Papers by Sepideh Nikookar
2023 IEEE 39th International Conference on Data Engineering (ICDE)
2022 IEEE 38th International Conference on Data Engineering (ICDE)
Proceedings of the 2022 International Conference on Management of Data
This work formalizes the Route Planning Problem (RPP), wherein a set of distributed assets (e.g.,... more This work formalizes the Route Planning Problem (RPP), wherein a set of distributed assets (e.g., ships, submarines, unmanned systems) simultaneously plan routes to optimize a team goal (e.g., find the location of an unknown threat or object in minimum time and/or fuel consumption) while ensuring that the planned routes satisfy certain constraints (e.g., avoiding collisions and obstacles). This problem becomes overwhelmingly complex for multiple distributed assets as the search space grows exponentially to design such plans. The RPP is formalized as a Team Discrete Markov Decision Process (TDMDP) and we propose a Multi-agent Multi-objective Reinforcement Learning (MaMoRL) framework for solving it. We investigate challenges in deploying the solution in real-world settings and study approximation opportunities. We experimentally demonstrate MaMoRL's effectiveness on multiple real-world and synthetic grids, as well as for transfer learning. MaMoRL is deployed for use by the
Proceedings of the Web Conference 2021
Uploads
Papers by Sepideh Nikookar