1 citations · 2 across the 2 of their papers we have counts for
4 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…
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…
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…