Reinforcement Learning

HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning

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

Ziniu Li, Yingru Li* (corresponding), Yushun Zhang, Tong Zhang, Zhi-Quan Luo

HyperDQN - Randomized Exploration for Deep Reinforcement Learning

Dec 14, 2021 12:00 AM NeurIPS 2021

Yingru LI

HyperDQN - Randomized Exploration for Deep Reinforcement Learning

Divergence-augmented policy optimization

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

Qing Wang*, Yingru Li* (equal), Jiechao Xiong, Tong Zhang