Yuxi Li

TL;DR: This work discusses core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration, and important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn.

TL;DR: An introduction to reinforcement learning and a selection of RL applications, including recommender systems, computer systems, energy, finance, healthcare, robotics, and transportation are discussed.

TL;DR: The proposed Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive learning method for unsupervised domain adaptive semantic segmentation, achieves the state-of-the-art performance on challenging benchmarks, outperforming previous methods by a large margin.

TL;DR: This work proposes a concise but effective ADA method called Select-by-Distinctive-Margin (SDM), which consists of a maximum margin loss and a margin sampling algorithm for data selection and provides theoretical analysis to show that SDM works like a Support Vector Machine.

TL;DR: In this article, a brief introduction to reinforcement learning (RL), and its relationship with deep learning, machine learning and AI is given, and a discussion is attempted, attempting to answer: “Why has RL not been widely adopted in practice yet?” and “When is RL helpful?’.