4 papers
Relative Entropy Pathwise Policy Optimization
Claas Voelcker, Axel Brunnbauer, Marcel Hussing +6
Score-function based methods for policy learning, such as REINFORCE and PPO, have delivered strong results in game-playing and robotics, yet their high variance often undermines tr…
Scalable Offline Reinforcement Learning for Mean Field Games
Axel Brunnbauer, Julian Lemmel, Zahra Babaiee +2
Reinforcement learning algorithms for mean-field games offer a scalable framework for optimizing policies in large populations of interacting agents. Existing methods often depend…
Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving
Axel Brunnbauer, Luigi Berducci, Peter Priller +2
The automated generation of diverse and complex training scenarios has been an important ingredient in many complex learning tasks. Especially in real-world application domains, su…
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing
Axel Brunnbauer, Luigi Berducci, Andreas Brandstätter +4
World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms. While learning world models for high-dim…