Area of research
Artificial Intelligence · Computer Vision and Pattern Recognition
Research interest
Research topics from publications: Curriculum Adversarial Training for Robust Reinforcement Learning. Representative work: Reinforcement learning with adversarial training is currently a key method for improving the robustness of DRL. However, in adversarial training, especially for unstable or disturbance-sensitive systems, the adversary always learns the policy significantly faster than the DRL agent and thus easily generates powerful perturbations. The agent cannot effectively adapt to the overly powerful adversary, which leads to unstable training and even failure to learn the robust policy. In this work, we propose a novel adversarial training method, called Curriculum Adversarial Training, inspired by the idea of curriculum learning. The method dynamically adjusts the strength of the adversary through natu