On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+($λ$,$λ$))-GA

• URL: http://arxiv.org/abs/2502.20265v2
• Date: Mon, 03 Mar 2025 20:00:38 GMT
• Title: On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+($λ$,$λ$))-GA
• Authors: Tai Nguyen, Phong Le, André Biedenkapp, Carola Doerr, Nguyen Dang,
• Abstract summary: We propose the application of a reward shaping mechanism to facilitate enhanced exploration of the environment by the RL agent.<n>Our work demonstrates the ability of RL in dynamically configuring the $(lambda,lambda)$-GA, but also confirms the advantages of reward shaping in the scalability of RL agents.
• Score: 7.924445204088514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamic Algorithm Configuration (DAC) has garnered significant attention in recent years, particularly in the prevalence of machine learning and deep learning algorithms. Numerous studies have leveraged the robustness of decision-making in Reinforcement Learning (RL) to address the optimization challenges associated with algorithm configuration. However, making an RL agent work properly is a non-trivial task, especially in reward design, which necessitates a substantial amount of handcrafted knowledge based on domain expertise. In this work, we study the importance of reward design in the context of DAC via a case study on controlling the population size of the $(1+(\lambda,\lambda))$-GA optimizing OneMax. We observed that a poorly designed reward can hinder the RL agent's ability to learn an optimal policy because of a lack of exploration, leading to both scalability and learning divergence issues. To address those challenges, we propose the application of a reward shaping mechanism to facilitate enhanced exploration of the environment by the RL agent. Our work not only demonstrates the ability of RL in dynamically configuring the $(1+(\lambda,\lambda))$-GA, but also confirms the advantages of reward shaping in the scalability of RL agents across various sizes of OneMax problems.

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