An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments

Autor:	Ding, Kewen, Vamplew, Peter, Foale, Cameron, Dazeley, Richard
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	One common approach to solve multi-objective reinforcement learning (MORL) problems is to extend conventional Q-learning by using vector Q-values in combination with a utility function. However issues can arise with this approach in the context of stochastic environments, particularly when optimising for the Scalarised Expected Reward (SER) criterion. This paper extends prior research, providing a detailed examination of the factors influencing the frequency with which value-based MORL Q-learning algorithms learn the SER-optimal policy for an environment with stochastic state transitions. We empirically examine several variations of the core multi-objective Q-learning algorithm as well as reward engineering approaches, and demonstrate the limitations of these methods. In particular, we highlight the critical impact of the noisy Q-value estimates issue on the stability and convergence of these algorithms. Comment: arXiv admin note: substantial text overlap with arXiv:2211.08669
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.03163 Zobrazit plný text záznamu View this record from Arxiv