| ## Robot Volleyball | |
| A simple minigame arcade-style Volleyball example with AI vs AI and Human VS AI modes. | |
| ### Observations: | |
| - Position of the ball in robot's local reference, | |
| - Position of the ball in the robot's goal's local reference (to tell on what side of the field the ball is in), | |
| - Velocity of the robot in robot's local reference, | |
| - Velocity of the ball in robot's local reference, | |
| - Whether the jump sensor is colliding (which means that the robot can jump), | |
| - Whether the robot is serving, | |
| - Whether the ball has been served, | |
| - Hit count of the ball in a row (hitting the ball more than 2 times in a row by the same robot causes a fault), | |
| - How many steps has passed without hitting the ball (only counted if serving, there is a time limit in that case). | |
| ### Action space: | |
| ```gdscript | |
| func get_action_space() -> Dictionary: | |
| return { | |
| "jump": {"size": 1, "action_type": "continuous"}, | |
| "movement": {"size": 1, "action_type": "continuous"} | |
| } | |
| ``` | |
| ### Rewards: | |
| - Positive reward for hitting the ball once when serving, | |
| - Negative reward if the same robot hits the ball more than 2 times in a row, | |
| - Negative reward if the ball hits the robots own goal | |
| ### Game over / episode end conditions: | |
| In infinite game mode or training mode, there are no specified end conditions. | |
| It's possible to disable these modes in the GameScene node in which case a winner will be announced, | |
| and the scores will be restarted, after a certain amount of points is reached. | |
| ### Running inference: | |
| #### AI vs AI | |
| Open the scene `res://scenes/testing_scenes/ai_vs_ai.tscn` in Godot Editor, and press `F6` or click on `Run Current Scene`. | |
| #### Human vs AI | |
| To play VS the AI, open the scene `res://scenes/testing_scenes/human_vs_ai.tscn` in Godot Editor, and press `F6` or click on `Run Current Scene`. | |
| Controls (you can adjust them in Project Settings in Godot Editor): | |
|  | |
| ### Training: | |
| The default scene `res://scenes/training_scene/training_scene.tscn` can be used for training. | |
| These were the parameters used to train the included onnx file (they can be applied by modifying [stable_baselines3_example.py](https://github.com/edbeeching/godot_rl_agents/blob/main/examples/stable_baselines3_example.py)): | |
| ```python | |
| policy_kwargs = dict(log_std_init=log(1.0)) | |
| model: PPO = PPO("MultiInputPolicy", env, verbose=1, n_epochs=10, learning_rate=0.0003, clip_range=0.2, ent_coef=0.0085, n_steps=128, batch_size=160, policy_kwargs=policy_kwargs, tensorboard_log=args.experiment_dir) | |
| ``` | |
| The arguments provided to the example for training were (feel free to adjust these): | |
| ```bash | |
| --timesteps=6_500_000 | |
| --n_parallel=5 | |
| --speedup=15 | |
| --env_path=[write the path to exported exe file here or remove this and n_parallel above for in-editor training] | |
| --onnx_export_path=volleyball.onnx | |
| ``` | |