This contains an air hockey simulation environment powered by Box2D. It is fast (C++ back-end), capable of self-play, 1v1 play, and easy goal-conditioned reinforcement learning, resulting in a rich testbed for various algorithms.

1v1 Play	Goal-Conditioned RL

• pip install Box2D
• pip install opencv-python

• pip install stable-baselines3 (required for play_trained_agent.py, sb_trainer.py, sb_eval.py)

Most of the files use a configuration file (--cfg cmd argument), but is defaulted to one from configs/. Please see there to tune parameters for various scripts.

• airhockey2d.py: base gym environment for air hockey
• render.py: renders the air hockey environment
• demonstrate.py: user plays a self-play air hockey environment using keyboard
• sb_trainer.py: trains an agent using self-play via stable-baselines3 PPO.
• sb_eval.py: run after training, this shows training evaluation plots and plays a live rendering of the trained agent playing via self-play.
• play_trained_agent: run after training, you can play against the trained agent