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Found 5 papers, 0 papers with code
Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control
This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services.
Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles
The RAIL is a novel derivative-free imitation learning method for autonomous driving with various ADAS functions coordination; and thus it imitates the operation of decision maker that controls autonomous driving with various ADAS functions.
Adversarial Imitation Learning via Random Search
As a result, research on imitation learning, which learns policy from a demonstration of experts, has begun to attract attention.
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL).
Randomized Adversarial Imitation Learning for Autonomous Driving
With the evolution of various advanced driver assistance system (ADAS) platforms, the design of autonomous driving system is becoming more complex and safety-critical.
