Reinforcement Learning

Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent

Addressing data and computation efficiency challenges in real-world deployments of RL Agents. It achieves significant efficiency gains in deep RL benchmarks as well as theoretical milestones.

Yingru Li, Jiawei Xu, Lei Han, Zhiquan Luo

Prior-dependent analysis of posterior sampling reinforcement learning with function approximation

Has implications on how the integration of prior knowledge enhances the efficiency of RL agents without extensive online exploration.

Yingru Li, Zhi-Quan Luo

HyperAgent - A Simple, Efficient, Scalable and Provable RL Framework

Practically and provably efficient RL under resource constraints!

Mar 23, 2024 1:30 PM Rice University

Yingru LI

HyperAgent - A Simple, Efficient and Scalable RL Framework for Complex Environments

Practically and provably efficient RL under resource constraints!

Jan 13, 2024 1:20 PM Daoyuan Building

Yingru LI

HyperAgent - A Simple, Efficient and Scalable RL Framework for Complex Environments

Towards AGI for Humanity through Efficient Reinforcement Learning

Addressing efficiency chanllenge in RL by HyperFQI algorithm

Oct 21, 2023 2:30 PM Teaching B Building

Yingru LI

Towards AGI for Humanity through Efficient Reinforcement Learning

HyperDQN - Randomized Exploration for Deep Reinforcement Learning

Dec 14, 2021 12:00 AM NeurIPS 2021

Yingru LI

HyperDQN - Randomized Exploration for Deep Reinforcement Learning

Hidden community detection in social networks

Kun He, Yingru Li, Sucheta Soundarajan, John E Hopcroft