47 citations · 62 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
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…