7 citations · 27 across the 8 of their papers we have counts for
Showing 2017 · cs.LGShow all
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cs.LG2017★ 6 cited
A Function Approximation Method for Model-based High-Dimensional Inverse Reinforcement Learning
Kun Li, Joel W. Burdick
This works handles the inverse reinforcement learning problem in high-dimensional state spaces, which relies on an efficient solution of model-based high-dimensional reinforcement…
cs.LG2017★ 4 cited
Inverse Reinforcement Learning in Large State Spaces via Function Approximation
Kun Li, Joel W. Burdick
This paper introduces a new method for inverse reinforcement learning in large-scale and high-dimensional state spaces. To avoid solving the computationally expensive reinforcement…
cs.LG2017★ 2 cited
Bellman Gradient Iteration for Inverse Reinforcement Learning
Kun Li, Yanan Sui, Joel W. Burdick
This paper develops an inverse reinforcement learning algorithm aimed at recovering a reward function from the observed actions of an agent. We introduce a strategy to flexibly han…