21 citations · 22 across the 4 of their papers we have counts for
4 papers
iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning
Aidan Scannell, Kalle Kujanpää, Yi Zhao +3
Learning representations for reinforcement learning (RL) has shown much promise for continuous control. We propose an efficient representation learning method using only a self-sup…
Challenges of Data-Driven Simulation of Diverse and Consistent Human Driving Behaviors
Kalle Kujanpää, Daulet Baimukashev, Shibei Zhu +4
Building simulation environments for developing and testing autonomous vehicles necessitates that the simulators accurately model the statistical realism of the real-world environm…
Suicidal Pedestrian: Generation of Safety-Critical Scenarios for Autonomous Vehicles
Yuhang Yang, Kalle Kujanpaa, Amin Babadi +2
Developing reliable autonomous driving algorithms poses challenges in testing, particularly when it comes to safety-critical traffic scenarios involving pedestrians. An open questi…
Automating Privilege Escalation with Deep Reinforcement Learning
Kalle Kujanpää, Willie Victor, Alexander Ilin
AI-based defensive solutions are necessary to defend networks and information assets against intelligent automated attacks. Gathering enough realistic data for training machine lea…