76 citations · 159 across the 9 of their papers we have counts for
12 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…
Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning
Simon Wiedemann, Daniel Hein, Steffen Udluft +1
We present a full implementation and simulation of a novel quantum reinforcement learning method. Our work is a detailed and formal proof of concept for how quantum algorithms can…
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning
Phillip Swazinna, Steffen Udluft, Daniel Hein +1
Offline reinforcement learning (RL) Algorithms are often designed with environments such as MuJoCo in mind, in which the planning horizon is extremely long and no noise exists. We…
Trustworthy AI for Process Automation on a Chylla-Haase Polymerization Reactor
Daniel Hein, Daniel Labisch
In this paper, genetic programming reinforcement learning (GPRL) is utilized to generate human-interpretable control policies for a Chylla-Haase polymerization reactor. Such contin…
Behavior Constraining in Weight Space for Offline Reinforcement Learning
Phillip Swazinna, Steffen Udluft, Daniel Hein +1
In offline reinforcement learning, a policy needs to be learned from a single pre-collected dataset. Typically, policies are thus regularized during training to behave similarly to…
Interpretable Control by Reinforcement Learning
Daniel Hein, Steffen Limmer, Thomas A. Runkler
In this paper, three recently introduced reinforcement learning (RL) methods are used to generate human-interpretable policies for the cart-pole balancing benchmark. The novel RL m…