This repository provides a simulation and training framework for quadruped locomotion tasks using deep reinforcement learning. It implements a hierarchical control architecture combining a high-level policy network with a low-level model predictive controller, trained in parallel using NVIDIA Isaac Gym with PyTorch. The framework supports multiple Unitree robot models including Go1, A1, and Aliengo, and is designed to facilitate transfer from simulation to real hardware.