76 citations · 159 across the 9 of their papers we have counts for
3 papers · 1 filter
Interpretable Policies for Reinforcement Learning by Genetic Programming
Daniel Hein, Steffen Udluft, Thomas A. Runkler
The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deplo…
A Benchmark Environment Motivated by Industrial Control Problems
Daniel Hein, Stefan Depeweg, Michel Tokic +4
In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are…
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
Daniel Hein, Steffen Udluft, Michel Tokic +3
The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks…