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

arXiv: https://arxiv.org/abs/2507.00762 · HTML: https://mlsanity.com/paper/2507.00762/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges](https://mlsanity.com/paper/2309.02473/) (2023)
- [imitation: Clean Imitation Learning Implementations](https://mlsanity.com/paper/2211.11972/) (2022)
- [ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation](https://mlsanity.com/paper/2307.02991/) (2023)
- [A Survey of Reinforcement Learning for Optimization in Automation](https://mlsanity.com/paper/2502.09417/) (2025)
- [SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process](https://mlsanity.com/paper/2503.10466/) (2025)
- [REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning](https://mlsanity.com/paper/2404.03359/) (2024)
- [Reinforcement Learning to improve delta robot throws for sorting scrap metal](https://mlsanity.com/paper/2406.13453/) (2024)
- [Genetic Imitation Learning by Reward Extrapolation](https://mlsanity.com/paper/2301.07182/) (2023)

## Built on by (most cited first, 3 of 3)

- [Leveraging Reinforcement Learning, Genetic Algorithms and Transformers for background determination in particle physics](https://mlsanity.com/paper/2509.14894/) (2025)
- [Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Control](https://mlsanity.com/paper/2510.20408/) (2025)
- [Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Control](https://mlsanity.com/paper/2603.26750/) (2026)

## Abstract (arXiv)

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.