Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task
Abstract
This paper presents a comparison between two well-known deep Reinforcement Learning (RL) algorithms: Deep Q-Learning (DQN) and Proximal Policy Optimization (PPO) in a simulated production system. We utilize a Petri Net (PN)-based simulation environment, which was previously proposed in related work. The performance of the two algorithms is compared based on several evaluation metrics, including average percentage of correctly assembled and sorted products, average episode length, and percentage of successful episodes. The results show that PPO outperforms DQN in terms of all evaluation metrics. The study highlights the advantages of policy-based algorithms in problems with high-dimensional state and action spaces. The study contributes to the field of deep RL in context of production systems by providing insights into the effectiveness of different algorithms and their suitability for different tasks.
Cite
@article{arxiv.2306.01451,
title = {Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task},
author = {Reuf Kozlica and Stefan Wegenkittl and Simon Hirländer},
journal= {arXiv preprint arXiv:2306.01451},
year = {2023}
}
Comments
Submitted and accepted version to the 32nd International Symposium on Industrial Electronics (ISIE), Helsinki, Finland