  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 learning problems. To solve the
computationally expensive reinforcement learning problems, we propose a
function approximation method to ensure that the Bellman Optimality Equation
always holds, and then estimate a function based on the observed human actions
for inverse reinforcement learning problems. The time complexity of the
proposed method is linearly proportional to the cardinality of the action set,
thus it can handle high-dimensional even continuous state spaces efficiently.
We test the proposed method in a simulated environment to show its accuracy,
and three clinical tasks to show how it can be used to evaluate a doctor's
proficiency.
