47 citations · 62 across the 4 of their papers we have counts for
4 papers
Learning Control Policies for Variable Objectives from Offline Data
Marc Weber, Phillip Swazinna, Daniel Hein +2
Offline reinforcement learning provides a viable approach to obtain advanced control strategies for dynamical systems, in particular when direct interaction with the environment is…
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…
Introduction to the "Industrial Benchmark"
Daniel Hein, Alexander Hentschel, Volkmar Sterzing +2
A novel reinforcement learning benchmark, called Industrial Benchmark, is introduced. The Industrial Benchmark aims at being be realistic in the sense, that it includes a variety o…