Name
Abstract | ||
|---|---|---|
Motivated by applications of multi-agent learning in noisy environments, this letter studies the robustness of gradient-based learning dynamics with respect to disturbances. While disturbances injected along a coordinate corresponding to any individual player's actions can always affect the overall learning dynamics, a subset of players can be disturbance decoupled-i.e., such players' actions are ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LCSYS.2020.3001240 | IEEE Control Systems Letters |
Keywords | DocType | Volume |
Games,Nash equilibrium,Robustness,Indexes,Machine learning,Couplings | Journal | 5 |
Issue | ISSN | Citations |
1 | IEEE Control Systems Letters, vol. 5, no. 1, pp. 223-228, Jan.
2021 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarah H. Q. Li | 1 | 0 | 0.34 |
| Lillian J. Ratliff | 2 | 38 | 4.86 |
| Behçet Açikmese | 3 | 41 | 15.88 |