4 citations · 4 across the 4 of their papers we have counts for
5 papers · 1 filter
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways
Branka Mirchevska, Maria Hügle, Gabriel Kalweit +2
Well-established optimization-based methods can guarantee an optimal trajectory for a short optimization horizon, typically no longer than a few seconds. As a result, choosing the…
Deep Inverse Q-learning with Constraints
Gabriel Kalweit, Maria Huegle, Moritz Werling +1
Popular Maximum Entropy Inverse Reinforcement Learning approaches require the computation of expected state visitation frequencies for the optimal policy under an estimate of the r…
Deep Constrained Q-learning
Gabriel Kalweit, Maria Huegle, Moritz Werling +1
In many real world applications, reinforcement learning agents have to optimize multiple objectives while following certain rules or satisfying a list of constraints. Classical met…
Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving
Maria Huegle, Gabriel Kalweit, Moritz Werling +1
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…
Dynamic Input for Deep Reinforcement Learning in Autonomous Driving
Maria Hügle, Gabriel Kalweit, Branka Mirchevska +2
In many real-world decision making problems, reaching an optimal decision requires taking into account a variable number of objects around the agent. Autonomous driving is a domain…