Multi-objective safe reinforcement learning

Abstract

Reinforcement learning (RL) is a learning method that learns actions based on trial and error. Recently, multi-objective reinforcement learning (MORL) and safe reinforcement learning (SafeRL) have been studied. The objective of conventional RL is to maximize the expected rewards; however, this may cause a fatal state because safety is not considered. Therefore, RL methods that consider safety during or after learning have been proposed. SafeRL is similar to MORL because it considers two objectives, i.e., maximizing expected rewards and satisfying safety constraints. However, to the best of our knowledge, no study has investigated the relationship between MORL and SafeRL to demonstrate that the SafeRL method can be applied to MORL tasks. This paper combines MORL with SafeRL and proposes a method for multi-objective safe RL (MOSafeRL). We applied the proposed method to Resource Gathering task, which is a standard task used in MORL test cases.