Titlebook: Distributed Artificial Intelligence; Second International Matthew E. Taylor,Yang Yu,Yang Gao Conference proceedings 2020 Springer Nature Sw

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书目名称Distributed Artificial Intelligence影响因子(影响力)




书目名称Distributed Artificial Intelligence影响因子(影响力)学科排名




书目名称Distributed Artificial Intelligence网络公开度




书目名称Distributed Artificial Intelligence网络公开度学科排名




书目名称Distributed Artificial Intelligence被引频次




书目名称Distributed Artificial Intelligence被引频次学科排名




书目名称Distributed Artificial Intelligence年度引用




书目名称Distributed Artificial Intelligence年度引用学科排名




书目名称Distributed Artificial Intelligence读者反馈




书目名称Distributed Artificial Intelligence读者反馈学科排名

Dysarthria

garrulous

chronology

知识

凝视

Andreev, Alexander Ignatyevich (1887–1959), in which the performance of selected motions are evaluated and used as the references for making decisions in the following updates. The efficiency of the proposed approaches were demonstrated through the experiments for three commonly considered scenarios, where the comparisons have been made with several well studied strategies.

凝视

Parallel Algorithm for Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Sresults on a 4-player stochastic game motivated by a naval strategic planning scenario, showing that our algorithm is able to quickly compute strategies constituting Nash equilibrium up to a very small degree of approximation error.

VAN

LAC-Nav: Collision-Free Multiagent Navigation Based on the Local Action Cells,, in which the performance of selected motions are evaluated and used as the references for making decisions in the following updates. The efficiency of the proposed approaches were demonstrated through the experiments for three commonly considered scenarios, where the comparisons have been made with several well studied strategies.

Interlocking

https://doi.org/10.1007/978-3-319-24237-8lated robotics environments show that our method enables more efficient and generalized meta-learning from past experience and outperforms state-of-the-art meta-RL and Hierarchical-RL methods in sparse reward settings.

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