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Cooperative and Competitive Reinforcement and Imitation Learning for a Mixture of Heterogeneous Learning Modules
This paper proposes Cooperative and competitive Reinforcement And Imitation Learning (CRAIL) for selecting an appropriate policy from a set of multiple heterogeneous modules and training all of them in parallel. Each learning module has its own network architecture and improves the policy based on a...
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| 出版年: | Front Neurorobot |
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| 第一著者: | |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Frontiers Media S.A.
2018
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6170616/ https://ncbi.nlm.nih.gov/pubmed/30319389 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnbot.2018.00061 |
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