no code implementations • 21 Mar 2022 • Branka Mirchevska, Moritz Werling, Joschka Boedecker
Implementing an autonomous vehicle that is able to output feasible, smooth and efficient trajectories is a long-standing challenge.
no code implementations • 6 Dec 2020 • Branka Mirchevska, Maria Hügle, Gabriel Kalweit, Moritz Werling, Joschka Boedecker
Well-established optimization-based methods can guarantee an optimal trajectory for a short optimization horizon, typically no longer than a few seconds.
no code implementations • 21 Oct 2020 • Maria Kalweit, Gabriel Kalweit, Moritz Werling, Joschka Boedecker
Challenging problems of deep reinforcement learning systems with regard to the application on real systems are their adaptivity to changing environments and their efficiency w. r. t.
2 code implementations • NeurIPS 2020 • Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker
In this work, we introduce a novel class of algorithms that only needs to solve the MDP underlying the demonstrated behavior once to recover the expert policy.
no code implementations • 20 Mar 2020 • Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker
We analyze the advantages of Constrained Q-learning in the tabular case and compare Constrained DQN to reward shaping and Lagrangian methods in the application of high-level decision making in autonomous driving, considering constraints for safety, keeping right and comfort.
no code implementations • 30 Sep 2019 • Maria Huegle, Gabriel Kalweit, Moritz Werling, Joschka Boedecker
The common pipeline in autonomous driving systems is highly modular and includes a perception component which extracts lists of surrounding objects and passes these lists to a high-level decision component.
no code implementations • 25 Jul 2019 • Maria Huegle, Gabriel Kalweit, Branka Mirchevska, Moritz Werling, Joschka Boedecker
In many real-world decision making problems, reaching an optimal decision requires taking into account a variable number of objects around the agent.