3 citations · 6 across the 7 of their papers we have counts for
9 papers
Iterative Batch Reinforcement Learning via Safe Diversified Model-based Policy Search
Amna Najib, Stefan Depeweg, Phillip Swazinna
Batch reinforcement learning enables policy learning without direct interaction with the environment during training, relying exclusively on previously collected sets of interactio…
TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning
Batıkan Bora Ormancı, Phillip Swazinna, Steffen Udluft +1
In this paper, we investigate offline reinforcement learning (RL) with the goal of training a single robust policy that generalizes effectively across environments with unseen dyna…
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…
Automatic Trade-off Adaptation in Offline RL
Phillip Swazinna, Steffen Udluft, Thomas Runkler
Recently, offline RL algorithms have been proposed that remain adaptive at runtime. For example, the LION algorithm \cite{lion} provides the user with an interface to set the trade…
User-Interactive Offline Reinforcement Learning
Phillip Swazinna, Steffen Udluft, Thomas Runkler
Offline reinforcement learning algorithms still lack trust in practice due to the risk that the learned policy performs worse than the original policy that generated the dataset or…
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…
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning
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 practiti…
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…
Overcoming Model Bias for Robust Offline Deep Reinforcement Learning
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. This makes it hard…