  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.
