Qiyang Li

Efficient Deep Reinforcement Learning Requires Regulating Overfitting

2023 · arXiv (Cornell University)

Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from limited amounts of data collected by actively interacting with the environment. While many prior works have shown that proper regularization techniques are crucial …

Graph Contrastive Learning with Adversarial Structure Refinement (GCL-ASR)

2024

The scarcity of labeled data in graph neural networks (GNNs) has driven the development of graph contrastive learning (GCL), which has become the most widely used method in unsupervised representation learning. At present, many GCL …