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

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書目名稱Distributed Artificial Intelligence影響因子(影響力)




書目名稱Distributed Artificial Intelligence影響因子(影響力)學(xué)科排名




書目名稱Distributed Artificial Intelligence網(wǎng)絡(luò)公開度




書目名稱Distributed Artificial Intelligence網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Distributed Artificial Intelligence被引頻次




書目名稱Distributed Artificial Intelligence被引頻次學(xué)科排名




書目名稱Distributed Artificial Intelligence年度引用




書目名稱Distributed Artificial Intelligence年度引用學(xué)科排名




書目名稱Distributed Artificial Intelligence讀者反饋




書目名稱Distributed Artificial Intelligence讀者反饋學(xué)科排名

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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.

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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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