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

Batch Reinforcement Learning on the Industrial Benchmark: First Experiences