Hyperparameter Optimization for Multi-Objective Reinforcement Learning
OPEN ACCESS
Loading...
Author / Producer
Date
2023-10-25
Publication Type
Working Paper
ETH Bibliography
no
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
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.
Permanent link
Publication status
published
Editor
Book title
Journal / series
Volume
Pages / Article No.
2310.16487
Publisher
Cornell University
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Computer Science - Machine Learning
Organisational unit
09828 - Fuge, Mark / Fuge, Mark
Notes
Presented at the MODeM workshop.