DyPS: Dynamic Parameter Sharing in Multi-Agent Reinforcement Learning for Spatio-Temporal Resource Allocation
Author:
Affiliation:
1. Department of EE, BNRist, Tsinghua University, Beijing, China
2. Huawei Noah's Ark Lab, Beijing, China
3. Tianjin University & Huawei Noah's Ark Lab, Beijing, China
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3672052
Reference51 articles.
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2. Stefano V Albrecht and Peter Stone. 2019. Reasoning about hypothetical agent behaviours and their parameters. arXiv preprint arXiv:1906.11064 (2019).
3. Spatio-Temporal Data Mining
4. Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, and Jan Kautz. 2016. Reinforcement learning through asynchronous advantage actor-critic on a gpu. arXiv preprint arXiv:1611.06256 (2016).
5. Strategic COVID-19 vaccine distribution can simultaneously elevate social utility and equity
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