Black-box, gradient-free optimization of car-racing policies.
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Updated
Oct 5, 2020 - Python
Black-box, gradient-free optimization of car-racing policies.
Stable dynamical system learning using Euclideanizing flows
Off-Policy Evaluation and Learning that is both Doubly Robust and Distributionally Robust.
[ECCV 2022] Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining
Experiment code for "Koopman Constrained Policy Optimization: a Koopman operator theoretic method for differentiable optimal control in robotics" as presented at ICML 2023
[ICLR 2023] Pytorch implementation of PPGeo, a fully self-supervised driving policy pre-training framework to learn from unlabeled driving videos.
Policy learning via doubly robust empirical welfare maximization over trees
PyTorch code for TAPAS-GMM.
[IEEE T-PAMI 2024] All you need for End-to-end Autonomous Driving
[RSS 2024] Learning Manipulation by Predicting Interaction
YLearn, a pun of "learn why", is a python package for causal inference
This repository implements Q-Learning in Blackjack, comparing it with random action selection and basic strategies. Includes experiments with various strategies, rule variations, and deck numbers to evaluate performance.
A curated list of 3D Vision papers relating to Robotics domain in the era of large models i.e. LLMs/VLMs, inspired by awesome-computer-vision, including papers, codes, and related websites
[CVPR 2024 Highlight] GenAD: Generalized Predictive Model for Autonomous Driving & Foundation Models in Autonomous System
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