Search Results for author:
Found 8 papers, 5 papers with code
Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
Imitation learning is an approach for generating intelligent behavior when the cost function is unknown or difficult to specify.
Parameter-Conditioned Sequential Generative Modeling of Fluid Flows
The computational cost associated with simulating fluid flows can make it infeasible to run many simulations across multiple flow conditions.
Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control
We introduce the Deep Variational Koopman (DVK) model, a method for inferring distributions over observations that can be propagated linearly in time.
Deep Dynamical Modeling and Control of Unsteady Fluid Flows
The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow.
Multi-Agent Imitation Learning for Driving Simulation
Simulation is an appealing option for validating the safety of autonomous vehicles.
Closed-Loop Policies for Operational Tests of Safety-Critical Systems
Manufacturers of safety-critical systems must make the case that their product is sufficiently safe for public deployment.
Simultaneous Policy Learning and Latent State Inference for Imitating Driver Behavior
In this work, we propose a method for learning driver models that account for variables that cannot be observed directly.
Imitating Driver Behavior with Generative Adversarial Networks
The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems.
