2 papers
cs.LG2026
FP-IRL: Fokker--Planck Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes
Chengyang Huang, Siddhartha Srivastava, Kenneth K. Y. Ho +4
Inverse reinforcement learning (IRL) is a powerful paradigm for uncovering the incentive structure that drives agent behavior, by inferring an unknown reward function from observed…
stat.ML2024
Variational Sequential Optimal Experimental Design using Reinforcement Learning
Wanggang Shen, Jiayuan Dong, Xun Huan
We present variational sequential optimal experimental design (vsOED), a novel method for optimally designing a finite sequence of experiments within a Bayesian framework with info…