Projects per year
Abstract
We present a novel model-based algorithm, Cooperative Prioritized Sweeping, for sample-efficient learning in large multi-agent Markov decision processes. Our approach leverages domain knowledge about the structure of the problem in the form of a dynamic decision network. Using this information, our method learns a model of the environment to determine which state-action pairs are the most likely in need to be updated, significantly increasing learning speed. Batch updates can then be performed which efficiently back-propagate knowledge throughout the value function. Our method outperforms the state-of-the-art sparse cooperative Q-learning and QMIX algorithms, both on the well-known SysAdmin benchmark, randomized environments and a fully-observable variation of the well-known firefighter benchmark from Dec-POMDP literature.
Original language | English |
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Title of host publication | Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021 |
Publisher | IFAAMAS |
Pages | 160-168 |
Number of pages | 9 |
ISBN (Electronic) | 9781713832621 |
DOIs | |
Publication status | Published - 2021 |
Event | The 20th International Conference on Autonomous Agents and Multiagent Systems - Virtual Duration: 3 May 2021 → 7 May 2021 https://aamas2021.soton.ac.uk/ |
Publication series
Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
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Volume | 1 |
ISSN (Print) | 1548-8403 |
ISSN (Electronic) | 1558-2914 |
Conference
Conference | The 20th International Conference on Autonomous Agents and Multiagent Systems |
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Abbreviated title | AAMAS 2021 |
Period | 3/05/21 → 7/05/21 |
Internet address |
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Project: Applied
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Project: Fundamental
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FWOSB27: Robust Fleet-Wide Reinforcement Learning
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Project: Fundamental