1 citations · 1 across the 4 of their papers we have counts for
4 papers
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
Dake Zhang, Boxiang Lyu, Shuang Qiu +2
We study risk-sensitive reinforcement learning (RL), a crucial field due to its ability to enhance decision-making in scenarios where it is essential to manage uncertainty and mini…
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
Shuang Qiu, Ziyu Dai, Han Zhong +3
This paper investigates posterior sampling algorithms for competitive reinforcement learning (RL) in the context of general function approximations. Focusing on zero-sum Markov gam…
On the Value of Myopic Behavior in Policy Reuse
Kang Xu, Chenjia Bai, Shuang Qiu +5
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence. In reinforcement learning, rationally reusing the policies acquired from other tasks or…
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
Shuang Qiu, Xiaohan Wei, Jieping Ye +2
While single-agent policy optimization in a fixed environment has attracted a lot of research attention recently in the reinforcement learning community, much less is known theoret…