2 papers
cs.LG2025
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…
cs.LG2024
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…