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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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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Frontiers Media S.A.
2018
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170616/ https://www.ncbi.nlm.nih.gov/pubmed/30319389 http://dx.doi.org/10.3389/fnbot.2018.00061 |