- Abstract:
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Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing multi-agent RL methods typically scale poorly in the problem size. Therefore, a key challenge is to translate the success of deep learning on singleagent RL to the multi-agent setting. A key stumbling block is that independent Q-learning, the most popular multi-agent RL method, introduces nonstationarity that makes it ...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
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(pdf, 577.7KB)
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- Publisher:
- PMLR Publisher's website
- Volume:
- 70
- Pages:
- 1146-1155
- Series:
- Proceedings of Machine Learning Research
- Publication date:
- 2017-07-29
- Acceptance date:
- 2017-05-12
- Pubs id:
-
pubs:695422
- URN:
-
uri:2b650b3b-2fce-4875-b4df-70f4a4d64c8a
- UUID:
-
uuid:2b650b3b-2fce-4875-b4df-70f4a4d64c8a
- Local pid:
- pubs:695422
- Copyright holder:
- Foerster et al.
- Copyright date:
- 2017
- Notes:
-
Copyright © 2017
by the authors. This is the accepted manuscript version of the article. The final version is available online from PMLR at: http://proceedings.mlr.press/v70/foerster17b.html
Conference item
Stabilising experience replay for deep multi-agent reinforcement learning
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