Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning

Understand

This paper investigates multi-objective reinforcement learning (MORL), which focuses on learning Pareto optimal policies in the presence of multiple reward functions.

• Despite MORL's significant empirical success, there is still a lack of satisfactory understanding of various MORL optimization targets and efficient learning algorithms.
• Our work offers a systematic analysis of several optimization targets to assess their abilities to find all Pareto optimal policies and controllability over learned policies by the preferences for different objectives.
• We then identify Tchebycheff scalarization as a favorable scalarization method for MORL.