agent

We prove HyperAgent closes a theoretical gap in scalable exploration. Further, GPT-HyperAgent addresses risk and efficiency challenges in human-Al interplay for automated content moderation with human feedback.

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.

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

Game-theoretic decision-making in multi-agent systems. I developed optimistic TS type algorithm that significantly reduce experimental costs in applications such as traffic management and radar communications.

TL;DR: We design a practical randomized exploration method to address the sample efficiency issue in online reinforcement learning.

Stabilizing policy optimization when off-policy data are reused, addressing the data efficiency issue in RL for real-world problems.