Toronto Intelligent Systems Lab
Toronto Intelligent Systems Lab
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Wilko Schwarting
Latest Publications
Dynamic Multi-Team Racing: Competitive Driving on 1/10-th Scale Vehicles via Learning in Simulation
Solving Continuous Control via Q-Learning
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
Learning Interactive Driving Policies via Data-driven Simulation
VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles
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Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Strength Through Diversity: Robust Behavior Learning via Mixture Policies
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
Deep Orientation Uncertainty Learning based on a Bingham Loss
Range-based Cooperative Localization with Nonlinear Observability Analysis
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