Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
This work demonstrates the feasibility of PSO-P for real-world industrial reinforcement learning applications, though it is incremental as it extends an existing method to a new benchmark.
The paper applied the Particle Swarm Optimization Policy (PSO-P) to the Industrial Benchmark, a realistic reinforcement learning benchmark with continuous spaces and stochasticity, and found that PSO-P outperformed established methods like RCNN and NFQ, yielding the best-performing policy with high robustness and low parameter tuning effort.
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.