paper-conference

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.

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.