Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems
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| Publicado no: | arXiv.org (Dec 20, 2024), p. n/a |
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| Autor principal: | |
| Outros Autores: | , |
| Publicado em: |
Cornell University Library, arXiv.org
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| Acesso em linha: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3148684109 | ||
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| 022 | |a 2331-8422 | ||
| 035 | |a 3148684109 | ||
| 045 | 0 | |b d20241220 | |
| 100 | 1 | |a Holder, Joshua | |
| 245 | 1 | |a Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 20, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Assignment problems are a classic combinatorial optimization problem in which a group of agents must be assigned to a group of tasks such that maximum utility is achieved while satisfying assignment constraints. Given the utility of each agent completing each task, polynomial-time algorithms exist to solve a single assignment problem in its simplest form. However, in many modern-day applications such as satellite constellations, power grids, and mobile robot scheduling, assignment problems unfold over time, with the utility for a given assignment depending heavily on the state of the system. We apply multi-agent reinforcement learning to this problem, learning the value of assignments by bootstrapping from a known polynomial-time greedy solver and then learning from further experience. We then choose assignments using a distributed optimal assignment mechanism rather than by selecting them directly. We demonstrate that this algorithm is theoretically justified and avoids pitfalls experienced by other RL algorithms in this setting. Finally, we show that our algorithm significantly outperforms other methods in the literature, even while scaling to realistic scenarios with hundreds of agents and tasks. | |
| 653 | |a Task scheduling | ||
| 653 | |a Multiagent systems | ||
| 653 | |a Algorithms | ||
| 653 | |a Satellite constellations | ||
| 653 | |a Machine learning | ||
| 653 | |a Combinatorial analysis | ||
| 653 | |a Polynomials | ||
| 653 | |a Optimization | ||
| 653 | |a Greedy algorithms | ||
| 700 | 1 | |a Jaques, Natasha | |
| 700 | 1 | |a Mesbahi, Mehran | |
| 773 | 0 | |t arXiv.org |g (Dec 20, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3148684109/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.15573 |