Maximilian Schier — accepted AI-conference papers

2025

Explainable Reinforcement Learning via Dynamic Mixture Policies

Maximilian Schier, Frederik Schubert, Bodo Rosenhahn

ICRA 2025

Learning control policies using deep reinforcement learning has shown great success for a variety of applications, including robotics and automated driving. A key area limiting the adaptation of RL in the real world is the lack of trust in the decision-making process of such policies. Therefore, exp

Cited by 1 Source ↗Scholar ↗

2023

Deep Reinforcement Learning for Autonomous Driving using High-Level Heterogeneous Graph Representations

Maximilian Schier, Christoph Reinders, Bodo Rosenhahn

ICRA 2023poster

Graph networks have recently been used for decision making in automated driving tasks for their ability to capture a variable number of traffic participants. Current high-level graph-based approaches, however, do not model the entire road network and thus must rely on handcrafted features for vehicl…

Cited by 14 Source ↗Scholar ↗