Least absolute policy iteration - A robust approach to value function approximation

Least-squares policy iteration is a useful reinforcement learning method in robotics due to its computational e?ciency. However, it tends to be sensitive to outliers in observed rewards. In this paper, we propose an alternative method that employs the absolute loss for enhancing robustness and reliability. The proposed method is formulated as a linear programming problem which can be solved eficiently by standard optimization software, so the computational advantage is not sacrificed for gaining robustness and reliability. We demonstrate the usefulness of the proposed approach through a simulated robot-control task. Copyright © 2010 The Institute of Electronics, Information and Communication Engineers.