A Collection of Foundation Driving Models by OpenDriveLab
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.
[RSS 2024] Learning Manipulation by Predicting Interaction
[ECCV 2022] Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining
Stable dynamical system (motion policy) learning using Euclideanizing flows
Black-box, gradient-free optimization of car-racing policies.
The official repository for Net Actor-Critic
[ECAI-2025] SPOWL: A JAX-based Safe RL framework that adaptively combines planning and policy learning with dynamic safety thresholds.
Policy-Latent Diffusion Network (PLD-Net) for multi-country content rating prediction. Achieves 80.6% accuracy with novel uncertainty-weighted ensemble and interpretable policy factors.
Implementation of a basic diffusion policy in jax with a full pipeline of data collection -> data augmentation -> training -> inference/evaluation
Neural network and reinforcement learning models for efficient decision-making on classical planning benchmarks
Add a description, image, and links to the policy-learning topic page so that developers can more easily learn about it.
To associate your repository with the policy-learning topic, visit your repo's landing page and select "manage topics."