All you need for End-to-end Autonomous Driving
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Updated
May 6, 2024
All you need for End-to-end Autonomous Driving
[Incl. GenAD, CVPR 2024 Highlight] Embracing Foundation Models into Autonomous Agent and System
YLearn, a pun of "learn why", is a python package for causal inference
[ICLR 2023] Pytorch implementation of PPGeo, a fully self-supervised driving policy pre-training framework to learn from unlabeled driving videos.
[ECCV 2022] Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining
Policy learning via doubly robust empirical welfare maximization over trees
Stable dynamical system learning using Euclideanizing flows
Off-Policy Evaluation and Learning that is both Doubly Robust and Distributionally Robust.
Black-box, gradient-free optimization of car-racing policies.
Experiment code for "Koopman Constrained Policy Optimization: a Koopman operator theoretic method for differentiable optimal control in robotics" as presented at ICML 2023
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