Distributed Learning for Planning Under Uncertainty Problems with Heterogeneous Teams
This paper considers the problem of multiagent sequential decision making under uncertainty and incomplete knowledge of the state transition model. A distributed learning framework, where each agent learns an individual model and shares the results with the team, is proposed. The challenges associat...
| Hlavní autoři: | , , , , |
|---|---|
| Další autoři: | |
| Médium: | Článek |
| Jazyk: | English |
| Vydáno: |
Springer Netherlands
2016
|
| On-line přístup: | http://hdl.handle.net/1721.1/103618 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0003-3756-3256 |