Multi-Objective Reinforcement Learning Application in the View of Agent AI Safety

Abstract

Reinforcement learning is frequently used to make single decisions using a single decision-based algorithm, where a single objective function is solved, for example, in an agent playing chess, the movement of any player is observed from previous action by considering whether it has received positive or negative reward based on this it will proceed. Nowadays, the requirement of making sequential decisions is complex for this reason. Learning involves using two or more objective functions to address a challenge in multiobjective reinforcement. As an illustration, consider a client purchasing a car and maximising conveniences like minimising fuel use, keeping costs low, and fitting additional luxury items in the automobile. Multi-objective reinforcement learning is applicable in decision-making sequential problem-solving applications. Still, at the same time, we need to avoid the risk that Artificial intelligence agents will be having when problem-solving using MOO-based algorithms. In this paper, we discuss how we use Multi-Objective Optimization (MOO)- based algorithm to low-impact agents for artificial intelligence (AI) safety.