Hyperparameter Optimization for Multi-Objective Reinforcement Learning

TL;DR

This paper investigates hyperparameter optimization for multi-objective reinforcement learning, formalizing the problem, proposing a methodology, and demonstrating its effectiveness in improving agent performance.

Contribution

It introduces a systematic approach to hyperparameter tuning in MORL, addressing a gap in current research and providing initial promising results.

Findings

Hyperparameter optimization improves MORL agent performance

Proposed methodology effectively identifies beneficial hyperparameters

Study highlights future research directions in MORL hyperparameter tuning

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.