A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis

• URL: http://arxiv.org/abs/2302.04179v1
• Date: Wed, 8 Feb 2023 16:38:55 GMT
• Title: A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis
• Authors: Mohsen Amidzadeh
• Abstract summary: Many sequential decision-making problems need optimization of different objectives which possibly conflict with each other. We develop a single-agent scale-independent multi-objective reinforcement learning on the basis of the Advantage Actor-Critic (A2C) algorithm. A convergence analysis is then done for the devised multi-objective algorithm providing a convergence-in-mean guarantee.
• Score: 0.6091702876917281
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Many sequential decision-making problems need optimization of different objectives which possibly conflict with each other. The conventional way to deal with a multi-task problem is to establish a scalar objective function based on a linear combination of different objectives. However, for the case of having conflicting objectives with different scales, this method needs a trial-and-error approach to properly find proper weights for the combination. As such, in most cases, this approach cannot guarantee an optimal Pareto solution. In this paper, we develop a single-agent scale-independent multi-objective reinforcement learning on the basis of the Advantage Actor-Critic (A2C) algorithm. A convergence analysis is then done for the devised multi-objective algorithm providing a convergence-in-mean guarantee. We then perform some experiments over a multi-task problem to evaluate the performance of the proposed algorithm. Simulation results show the superiority of developed multi-objective A2C approach against the single-objective algorithm.

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