Decentralized online planning can be an attractive paradigm for cooperative multi-agent systems, due to improved scalability and robustness. A key difficulty of such approach lies in making accurate predictions about the decisions of other agents. In this paper, we present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search, combined with models of teammates learned from previous episodic runs. By only allowing one agent to adapt its models at a time, under the assumption of ideal policy approximation, successive iterations of our method are guaranteed to improve joint policies, and eventually lead to convergence to a Nash equilibrium. We test the efficiency of the algorithm by performing experiments in several scenarios of the spatial task allocation environment introduced in [Claes et al., 2015]. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators which exploit the spatial features of the problem, and that the proposed algorithm improves over the baseline planning performance for particularly challenging domain configurations.
|Title of host publication||Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020|
|Number of pages||8|
|Publication status||Published - 2020|
|Event||29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence - |
Duration: 5 Jan 2021 → 10 Jan 2021
|Name||IJCAI International Joint Conference on Artificial Intelligence|
|Conference||29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence|
|Abbreviated title||IJCAI-PRICAI 2020|
|Period||5/01/21 → 10/01/21|
Bibliographical noteVirtual/online event due to COVID-19 ? moved to January 2021
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
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