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

TL;DR

This paper explores using Genetic Algorithms to generate expert demonstrations that enhance reinforcement learning in industrial environments, leading to faster convergence and improved performance.

Contribution

It introduces a novel hybrid approach combining GAs with RL, demonstrating improved training efficiency and performance in a real-world inspired setting.

Findings

GA-generated demonstrations improve RL performance

PPO agents with GA data achieve higher rewards

Hybrid methods accelerate training convergence

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.