no code implementations • 11 Aug 2023 • Marc Weber, Phillip Swazinna, Daniel Hein, Steffen Udluft, Volkmar Sterzing
Offline reinforcement learning provides a viable approach to obtain advanced control strategies for dynamical systems, in particular when direct interaction with the environment is not available.
no code implementations • 16 Jun 2023 • Phillip Swazinna, Steffen Udluft, Thomas Runkler
Recently, offline RL algorithms have been proposed that remain adaptive at runtime.
1 code implementation • 21 May 2022 • Phillip Swazinna, Steffen Udluft, Thomas Runkler
At the same time, offline RL algorithms are not able to tune their most important hyperparameter - the proximity of the learned policy to the original policy.
1 code implementation • 14 Jan 2022 • Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas Runkler
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.
no code implementations • 26 Nov 2021 • Phillip Swazinna, Steffen Udluft, Thomas Runkler
Recently developed offline reinforcement learning algorithms have made it possible to learn policies directly from pre-collected datasets, giving rise to a new dilemma for practitioners: Since the performance the algorithms are able to deliver depends greatly on the dataset that is presented to them, practitioners need to pick the right dataset among the available ones.
1 code implementation • 12 Jul 2021 • Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas Runkler
In offline reinforcement learning, a policy needs to be learned from a single pre-collected dataset.
no code implementations • 12 Aug 2020 • Phillip Swazinna, Steffen Udluft, Thomas Runkler
State-of-the-art reinforcement learning algorithms mostly rely on being allowed to directly interact with their environment to collect millions of observations.