Abstract
Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. In this work, we empirically investigate the representational power of various network architectures on a series of one-shot games. Despite their simplicity, these games capture many of the crucial problems that arise in the multi-agent setting, such as an exponential number of joint actions or the lack of an explicit coordination mechanism. Our results quantify how well various approaches can represent the requisite value functions, and help us identify issues that can impede good performance.
Original language | English |
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Title of host publication | AAMAS'19 |
Subtitle of host publication | Proceedings of the Eighteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS) |
Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
Pages | 1862-1864 |
Number of pages | 3 |
ISBN (Print) | 978-1-4503-6309-9 |
Publication status | Published - 2019 |
Event | AAMAS 2019: The 18th International Conference on Autonomous Agents and MultiAgent Systems - Montreal, Canada Duration: 13 May 2019 → 17 May 2019 |
Conference
Conference | AAMAS 2019 |
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Country | Canada |
City | Montreal |
Period | 13/05/19 → 17/05/19 |
Keywords
- multi-agent systems
- neural networks
- decision-making
- actionvaluerepresentation
- one-shotgames