Seyde, Tim

14 publications

ICMLW 2024 Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution Tim Seyde, Peter Werner, Wilko Schwarting, Markus Wulfmeier, Daniela Rus

L4DC 2024 Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution Tim Seyde, Peter Werner, Wilko Schwarting, Markus Wulfmeier, Daniela Rus

CoRL 2023 Dynamic Multi-Team Racing: Competitive Driving on 1/10-Th Scale Vehicles via Learning in Simulation Peter Werner, Tim Seyde, Paul Drews, Thomas Matrai Balch, Igor Gilitschenski, Wilko Schwarting, Guy Rosman, Sertac Karaman, Daniela Rus

NeurIPS 2023 Gigastep - One Billion Steps per Second Multi-Agent Reinforcement Learning Mathias Lechner, Lianhao Yin, Tim Seyde, Tsun-Hsuan Johnson Wang, Wei Xiao, Ramin Hasani, Joshua Rountree, Daniela Rus

CoRL 2023 Measuring Interpretability of Neural Policies of Robots with Disentangled Representation Tsun-Hsuan Wang, Wei Xiao, Tim Seyde, Ramin Hasani, Daniela Rus

ICLR 2023 Solving Continuous Control via Q-Learning Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski, Martin Riedmiller, Daniela Rus, Markus Wulfmeier

L4DC 2022 Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Sertac Karaman, Daniela Rus

NeurIPS 2021 Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin Riedmiller, Markus Wulfmeier, Daniela Rus

CoRL 2021 Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus

NeurIPSW 2021 Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Sertac Karaman, Daniela Rus

CoRL 2021 Strength Through Diversity: Robust Behavior Learning via Mixture Policies Tim Seyde, Wilko Schwarting, Igor Gilitschenski, Markus Wulfmeier, Daniela Rus

NeurIPSW 2021 Strength Through Diversity: Robust Behavior Learning via Mixture Policies Tim Seyde, Wilko Schwarting, Igor Gilitschenski, Markus Wulfmeier, Daniela Rus

CoRL 2020 Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus

L4DC 2020 Learning to Plan via Deep Optimistic Value Exploration Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus