Joshua Rountree — accepted AI-conference papers

2024

Competitive Multi-Team Behavior in Dynamic Flight Scenarios

Tim Seyde, Mathias Lechner, Joshua Rountree, Daniela Rus

IROS 2024

Efficiently learning strategic multi-agent behavior remains a challenge for robotic systems deployed in real-world scenarios, especially when considering underactuated or dynamically unstable systems. Such systems demand an integrated approach that informs long-term strategic planning with constrain

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2023

Gigastep - One Billion Steps per Second Multi-agent Reinforcement Learning

Mathias Lechner, Lianhao Yin, Tim Seyde, Tsun-Hsuan Wang, Wei Xiao, Ramin Hasani +2

NeurIPS 2023poster

Multi-agent reinforcement learning (MARL) research is faced with a trade-off: it either uses complex environments requiring large compute resources, which makes it inaccessible to researchers with limited resources, or relies on simpler dynamics for faster execution, which makes the transferability…

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