Jiafan He

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation

2023 · arXiv (Cornell University)

We study multi-agent reinforcement learning in the setting of episodic Markov decision processes, where multiple agents cooperate via communication through a central server. We propose a provably efficient algorithm based on value iteration that enable …

Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning

2024 · Algorithms

Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifier using both …