9 citations · 11 across the 12 of their papers we have counts for
12 papers
What Probing Reveals about Autonomous Driving: Linking Internal Prediction Errors to Ego Planning
Hyeonchang Jeon, Kyungbeom Kim, Eugene Vinitsky +1
Large-scale datasets and fast simulators have enabled improvements in driving policies that appear safe and robust, yet strong performance in nominal scenarios can still mask flawe…
EMAgnet: Parameter-Space EMA Regularization for Policy Gradient Self-Play in Large Games
Tristan Maidment, JB Lanier, Chase McDonald +5
Recent work has established that regularized policy gradient methods such as PPO, when used in self-play, can match or exceed specialized game-theoretic algorithms for solving two-…
Scaling Self-Play for End-to-End Driving
Luke Rowe, Roger Girgis, Rodrigue de Schaetzen +6
End-to-end autonomous driving models are typically trained on offline human-demonstration datasets that provide limited state coverage and often no closed-loop feedback, making the…
Human-like autonomy emerges from self-play and a pinch of human data
Daphne Cornelisse, Julian Hunt, Zixu Zhang +4
Self-play reinforcement learning has recently emerged as a way to train driving policies without any human data. It uses cheap, large-scale simulations to substitute expensive, lar…
Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving
Aron Distelzweig, Faris Janjoš, Andreas Look +7
Recent Autonomous Driving (AD) works such as GigaFlow and PufferDrive have unlocked Reinforcement Learning (RL) at scale as a training strategy for driving policies. Yet such polic…
Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic
Saeed Rahmani, Shiva Rasouli, Daphne Cornelisse +3
Autonomous vehicles (AVs) are now operating on public roads, which makes their testing and validation more critical than ever. Simulation offers a safe and controlled environment f…
Scalable Decision Making for Games of Imperfect Information
Samuel Sokota, Eugene Vinitsky, Hengyuan Hu +3
Real-world decision-making generally involves hidden information, that is, information that is unknown to one agent but possessed by another. Unfortunately, the presence of large a…
Estimating cognitive biases with attention-aware inverse planning
Sounak Banerjee, Daphne Cornelisse, Deepak Gopinath +5
People's goal-directed behaviors are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, people's attention…
Building reliable sim driving agents by scaling self-play
Daphne Cornelisse, Aarav Pandya, Kevin Joseph +2
Simulation agents are essential for designing and testing systems that interact with humans, such as autonomous vehicles (AVs). These agents serve various purposes, from benchmarki…
GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse +2
Multi-agent learning algorithms have been successful at generating superhuman planning in various games but have had limited impact on the design of deployed multi-agent planners.…
Human-compatible driving partners through data-regularized self-play reinforcement learning
Daphne Cornelisse, Eugene Vinitsky
A central challenge for autonomous vehicles is coordinating with humans. Therefore, incorporating realistic human agents is essential for scalable training and evaluation of autono…
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world
Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang +2
We introduce Nocturne, a new 2D driving simulator for investigating multi-agent coordination under partial observability. The focus of Nocturne is to enable research into inference…