# Batch Reinforcement Learning on the Industrial Benchmark: First Experiences

arXiv: https://arxiv.org/abs/1705.07262 · HTML: https://mlsanity.com/paper/1705.07262/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2017.

## Built on by (most cited first, 5 of 5)

- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [User-Interactive Offline Reinforcement Learning](https://mlsanity.com/paper/2205.10629/) (2022)
- [Learning Control Policies for Variable Objectives from Offline Data](https://mlsanity.com/paper/2308.06127/) (2023)
- [A cGAN Ensemble-based Uncertainty-aware Surrogate Model for Offline Model-based Optimization in Industrial Control Problems](https://mlsanity.com/paper/2205.07250/) (2022)
- [From Overfitting to Reliability: Introducing the Hierarchical Approximate Bayesian Neural Network](https://mlsanity.com/paper/2512.13111/) (2025)

## Abstract (arXiv)

The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting. To further investigate the properties and feasibility on real-world applications, this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a novel reinforcement learning (RL) benchmark that aims at being realistic by including a variety of aspects found in industrial applications, like continuous state and action spaces, a high dimensional, partially observable state space, delayed effects, and complex stochasticity. The experimental results of PSO-P on IB are compared to results of closed-form control policies derived from the model-based Recurrent Control Neural Network (RCNN) and the model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not only of interest for academic benchmarks, but also for real-world industrial applications, since it also yielded the best performing policy in our IB setting. Compared to other well established RL techniques, PSO-P produced outstanding results in performance and robustness, requiring only a relatively low amount of effort in finding adequate parameters or making complex design decisions.