GYM_XPLANE_ML wraps the X-Plane flight simulator as an OpenAI Gym environment, exposing throttle, elevator, aileron and rudder controls as action spaces and flight parameters like altitude, speed, and orientation as observations. Users can script training workflows in Python, select predefined scenarios or customize waypoints, weather conditions, and aircraft models. The library handles low-latency communication with X-Plane, runs episodes in synchronous mode, logs performance metrics, and supports real-time rendering for debugging. It enables iterative development of ML-driven autopilots and experimental RL algorithms in a high-fidelity flight environment.

MAPF_G2RL is an open-source research framework that bridges graph theory and deep reinforcement learning to tackle the multi-agent path finding (MAPF) problem. It encodes nodes and edges into vector representations, defines spatial and collision-aware reward functions, and supports various RL algorithms such as DQN, PPO, and A2C. The framework automates scenario creation by generating random graphs or importing real-world maps, and orchestrates training loops that optimize policies for multiple agents simultaneously. After learning, agents are evaluated in simulated environments to measure path optimality, makespan, and success rates. Its modular design allows researchers to extend core components, integrate new MARL techniques, and benchmark against classical solvers.