4 papers · 1 filter
Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings
C. Shi, S. Zhang, W. Lu +1
Reinforcement learning is a general technique that allows an agent to learn an optimal policy and interact with an environment in sequential decision making problems. The goodness…
Doubly Inhomogeneous Reinforcement Learning
Liyuan Hu, Mengbing Li, Chengchun Shi +2
This paper studies reinforcement learning (RL) in doubly inhomogeneous environments under temporal non-stationarity and subject heterogeneity. In a number of applications, it is co…
Testing Stationarity and Change Point Detection in Reinforcement Learning
Mengbing Li, Chengchun Shi, Zhenke Wu +1
We consider offline reinforcement learning (RL) methods in possibly nonstationary environments. Many existing RL algorithms in the literature rely on the stationarity assumption th…
Sequential Knockoffs for Variable Selection in Reinforcement Learning
Tao Ma, Jin Zhu, Hengrui Cai +4
In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior…