Multi-objective path integral policy improvement for learning robotic motion

This paper proposes a new multi-objective reinforcement learning (MORL) algorithm for robotics by extending policy improvement with path integral (\(\text {PI}^2\)) algorithm. For a robot motion acquisition problem, most existing MORL algorithms are hard to apply, because of the high-dimensional and continuous state and action spaces. However, policy-based algorithms such as \(\text {PI}^2\) can be applied to solve this problem in single-objective cases. Based on the similarity of \(\text {PI}^2\) and evolution strategies (ESs) and the fact that ESs are well-suited for multi-objective optimization, we propose an extension of \(\text {PI}^2\) and some techniques to speed up the learning. The effectiveness is shown via numerical simulations.