This repository implements a deep reinforcement learning approach for decentralized multi-robot collision avoidance. It uses Proximal Policy Optimization (PPO) to train policies that enable robots to navigate crowded environments without collisions. The system supports curriculum learning with two training stages, progressing from simple to complex scenarios, and integrates with the ROS/Stage simulation environment. Training uses MPI for distributed computation across multiple processes.