Basic constrained RL agents used in experiments for the "Benchmarking Safe Exploration in Deep Reinforcement Learning" paper.

Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models

[NeurIPS 2024 Datasets and Benchmarks Track] Closed-Loop E2E-AD Benchmark Enhanced by World Model RL Expert

[CVPR 2025, Spotlight] SimLingo (CarLLava): Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment

Single-file implementation to advance vision-language-action (VLA) models with reinforcement learning.

PyTorch implementation for the paper "Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving"

Create an beautiful trajectory visualization of the autonomous vehicle using Python

PyTorch implementation of Constrained Reinforcement Learning for Soft Actor Critic Algorithm

Implementation Model Predictive Control with CARLA simulator based on Python

PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.

PyTorch implementation of the discrete Soft-Actor-Critic algorithm.