Name
Abstract | ||
|---|---|---|
Deep neural networks have demonstrated their capability to learn control policies for a variety of tasks. However, these neural network-based policies have been shown to be susceptible to exploitation by adversarial agents. Therefore, there is a need to develop techniques to learn control policies that are robust against adversaries. We introduce Adversarially Robust Control (ARC), which trains th... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ITSC48978.2021.9564579 | 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) |
Keywords | DocType | ISBN |
Training,Robust control,Road transportation,Reinforcement learning,Robustness,Safety,Task analysis | Conference | 978-1-7281-9142-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sampo Kuutti | 1 | 0 | 0.34 |
| Saber Fallah | 2 | 0 | 0.34 |
| Richard Bowden | 3 | 1840 | 118.50 |