Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866912007457865728 |
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| author | Soffair, Nitsan |
| author_facet | Soffair, Nitsan |
| contents | WQMIX, QMIX, QTRAN, and VDN are SOTA algorithms for Dec-POMDP. All of them cannot solve complex agents' cooperation domains. We give an algorithm to solve such problems. In the first stage, we solve a single-agent problem and get a policy. In the second stage, we solve the multi-agent problem with the single-agent policy. SA2MA has a clear advantage over all competitors in complex agents' cooperative domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_15411 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics Soffair, Nitsan Machine Learning Artificial Intelligence Multiagent Systems WQMIX, QMIX, QTRAN, and VDN are SOTA algorithms for Dec-POMDP. All of them cannot solve complex agents' cooperation domains. We give an algorithm to solve such problems. In the first stage, we solve a single-agent problem and get a policy. In the second stage, we solve the multi-agent problem with the single-agent policy. SA2MA has a clear advantage over all competitors in complex agents' cooperative domains. |
| title | Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics |
| topic | Machine Learning Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2211.15411 |