Hyperparameter Optimization for Multi-Objective Reinforcement Learning

Felten, Florian; Gareev, Daniel; Talbi, El-Ghazali; Danoy, Grégoire

Document type :

Rapport de recherche

DOI :

10.48550/arXiv.2310.16487

Title :

Hyperparameter Optimization for Multi-Objective Reinforcement Learning

Author(s) :

Felten, Florian [Auteur]
Université du Luxembourg = University of Luxembourg = Universität Luxemburg [uni.lu]
Gareev, Daniel [Auteur]
Talbi, El-Ghazali [Auteur]
Inria Lille - Nord Europe
Université de Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Optimisation de grande taille et calcul large échelle [BONUS]
Danoy, Grégoire [Auteur]
Université du Luxembourg = University of Luxembourg = Universität Luxemburg [uni.lu]

Publisher :

arXiv

Institution :

University of Luxembourg

Publication date :

2023

English keyword(s) :

Machine Learning (cs.LG)
FOS: Computer and information sciences

HAL domain(s) :

Informatique [cs]

English abstract : [en]

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex problems. The recent introduction of multi-objective reinforcement learning (MORL) has further expanded the scope of RL by enabling agents ...
Show more >Reinforcement learning (RL) has emerged as a powerful approach for tackling complex problems. The recent introduction of multi-objective reinforcement learning (MORL) has further expanded the scope of RL by enabling agents to make trade-offs among multiple objectives. This advancement not only has broadened the range of problems that can be tackled but also created numerous opportunities for exploration and advancement. Yet, the effectiveness of RL agents heavily relies on appropriately setting their hyperparameters. In practice, this task often proves to be challenging, leading to unsuccessful deployments of these techniques in various instances. Hence, prior research has explored hyperparameter optimization in RL to address this concern. This paper presents an initial investigation into the challenge of hyperparameter optimization specifically for MORL. We formalize the problem, highlight its distinctive challenges, and propose a systematic methodology to address it. The proposed methodology is applied to a well-known environment using a state-of-the-art MORL algorithm, and preliminary results are reported. Our findings indicate that the proposed methodology can effectively provide hyperparameter configurations that significantly enhance the performance of MORL agents. Furthermore, this study identifies various future research opportunities to further advance the field of hyperparameter optimization for MORL.Show less >

Language :

Anglais

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Files

2310.16487
Open access
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