  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.
