Self-evolving Autoencoder Embedded Q-Network

no code implementations • 18 Feb 2024 • J. Senthilnath, Bangjian Zhou, Zhen Wei Ng, Deeksha Aggarwal, Rajdeep Dutta, Ji Wei Yoon, Aye Phyu Phyu Aung, Keyu Wu, Min Wu, XiaoLi Li

During the evolution of the autoencoder architecture, a bias-variance regulatory strategy is employed to elicit the optimal response from the RL agent.

Decision Making Reinforcement Learning (RL)

DO-GAN: A Double Oracle Framework for Generative Adversarial Networks

no code implementations • CVPR 2022 • Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, XiaoLi Li

In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles.

Continual Learning