Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environments

• URL: http://arxiv.org/abs/2507.00762v2
• Date: Wed, 02 Jul 2025 14:06:48 GMT
• Title: Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environments
• Authors: Tom Maus, Asma Atamna, Tobias Glasmachers,
• Abstract summary: Reinforcement Learning (RL) has demonstrated significant potential in certain real-world industrial applications.<n>This study investigates the utilization of Genetic Algorithms (GAs) as a mechanism for improving RL performance.<n>We propose a novel approach in which GA-generated expert demonstrations are used to enhance policy learning.
• Score: 0.8602553195689513
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reinforcement Learning (RL) has demonstrated significant potential in certain real-world industrial applications, yet its broader deployment remains limited by inherent challenges such as sample inefficiency and unstable learning dynamics. This study investigates the utilization of Genetic Algorithms (GAs) as a mechanism for improving RL performance in an industrially inspired sorting environment. We propose a novel approach in which GA-generated expert demonstrations are used to enhance policy learning. These demonstrations are incorporated into a Deep Q-Network (DQN) replay buffer for experience-based learning and utilized as warm-start trajectories for Proximal Policy Optimization (PPO) agents to accelerate training convergence. Our experiments compare standard RL training with rule-based heuristics, brute-force optimization, and demonstration data, revealing that GA-derived demonstrations significantly improve RL performance. Notably, PPO agents initialized with GA-generated data achieved superior cumulative rewards, highlighting the potential of hybrid learning paradigms, where heuristic search methods complement data-driven RL. The utilized framework is publicly available and enables further research into adaptive RL strategies for real-world applications.

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