Inverse reinforcement learning using Dynamic Policy Programming
Eiji Uchibe, Kenji Doya · 2014
This paper proposes a novel model-free inverse reinforcement learning method based on density ratio estimation under the framework of Dynamic Policy Programming. We show that the logarithm of the ratio between the optimal policy and the baseline policy is represented by the state-dependent cost and the value function. Our proposal is to use density ratio estimation methods to estimate the density ratio of policies and the least squares method with regularization to estimate the state-dependent cost and the value function that satisfies the relation. Our method can avoid computing the integral such as evaluating the partition function. A simple numerical simulation of a grid world navigation, a car driving, and a pendulum swing-up shows its superiority over conventional methods.