shadow-rogue-self-play

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shadow-rogue-self-play

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Dodge projectiles with Self Play and RL in Unity & ML Agents

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# Self-Play RL Dodger

2D Unity ML-Agents game where two players dodge, jump, crouch, dash, and shoot bullets. One side can be human controlled, or both sides can run trained policies.
Play the game [here](https://avbiswas.itch.io/shadow-gone-rogue)

https://github.com/user-attachments/assets/1708ed58-ab74-49ba-beff-2e5dad0a0040

## Support

If you find this helpful, consider supporting on Patreon!

[<img src="https://c5.patreon.com/external/logo/become_a_patron_button.png" alt="Become a Patron!" width="200">](https://www.patreon.com/NeuralBreakdownwithAVB)

## Model

- **Included model:** `Assets/RLAgents/results/env_sp11/BulletShooter/BulletShooter-3101431.onnx`
- **Main config:** `Assets/RLAgents/config/selfPlay.yaml`
- **Network:** PPO self-play policy, feed-forward MLP, 3 hidden layers, 512 units per layer, no recurrent memory.

## What is Self-Play?

Self-play is Machine Learning technique where the AI plays against previous versions of itself till it gets better! During training, we save older policy snapshots and re-use them as opponents for our current model. This updates policies to be diversely capable against a variety of strategies.

## How to play locally

Open the project in Unity, then go to `File > Build Settings`.

Select `WebGL`, include `Scenes/MainMenu` and `Scenes/GameEnv`, then build to a folder such as `Builds-WebGL`.

For easiest local testing, set `Player Settings > Publishing Settings > Compression Format` to `Disabled`.

After the build finishes, serve the folder locally:

```bash
cd Builds-WebGL
python3 -m http.server 8080
```

Then open:

```text
http://localhost:8080
```

Play!!

## How to train

To train headlessly, first make a standalone Unity build. In Unity, use `File > Build Settings`, choose `Windows, Mac, Linux`, include `MainMenu` and `GameEnv`, then build to a local path such as `Builds/RLBuild.app`.

Then run ML-Agents against that build:

```bash
mlagents-learn Assets/RLAgents/config/selfPlay.yaml --run-id env_sp_new --env Builds/RLBuild.app --num-envs 8 --no-graphics
```

Training outputs go under `Assets/RLAgents/results/`. Keep only the final `.onnx` model and its `.meta` file in git.

## Environment

- **Initial state:** both players reset to their spawn positions with zero velocity, alive state, dash available, crouch off, and bullets reset.

- **State space:** player position, velocity, crouch state, dash state, dash cooldown, shot cooldown, plus ray perception sensors for nearby bullets, walls, ground, and opponent.

- **Action space:** five discrete controls: move left/right/idle, jump, crouch, dash, and shoot. Shooting is masked while crouching.

- **Reward space:** death gives `-1`, winning gives `+1`, and bullet dodge reward is currently `0`. Env is super sparse, which is great for self-play!

- **Terminal state:** a player dies by bullet, falling, or inactivity. The dead player ends the episode and the surviving player gets the win reward.

## Devlog

Playlist: https://www.youtube.com/playlist?list=PLGXWtN1HUjPdoJwzrCmfVCtOY2GN2kzEb

These were some of my first videos, so the audio is a bit weak.

## Training config

The shared policy run used PPO with self-play:

- **Learning rate:** `0.0002` - small enough to keep self-play training stable.
- **Batch size:** `2048` - large batch for smoother PPO updates.
- **Buffer size:** `20480` - collects enough experience before each update.
- **Time horizon:** `256` - lets rewards connect to longer dodge/shoot sequences.
- **Self-play window:** `10` - keeps a pool of older opponents.
- **Swap steps:** `5,000` - changes opponents often enough to avoid overfitting.
- **Opponent mix:** `0.5` latest model ratio - half recent opponent, half older snapshots.

## Controls (in human vs AI mode)

- **Move:** `A` / `D` or left / right
- **Jump:** space
- **Crouch:** down / `S`
- **Dash:** dash input from the Unity input map
- **Shoot:** `K`

## Note

This project predates vibe-coding. I made it to learn Unity and build something cool with self-play. There may be superfluous files, old experiments, and dead code.

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