---
library_name: stable-baselines3
tags:
  - reinforcement-learning
  - battleship
  - ppo
  - maskable-ppo
  - sb3-contrib
  - custom-environment
---

# Battleship PPO Agent — Hanks1234/battleship-ppo-dagger

A MaskablePPO agent trained on a 10x20 Battleship board with custom
T-shaped and Z-shaped ships using [sb3-contrib](https://sb3-contrib.readthedocs.io/).

## Environment

- **Board**: 10 columns x 20 rows
- **Ships**: 10 ships including T-shaped Battleships and Z-shaped Carriers
- **Observation**: 5-channel binary image (5, 20, 10)
- **Action**: Discrete(200) with action masking (no repeat shots)

## Training Config

| Parameter | Value |
|-----------|-------|
| `method` | `DAgger (Dataset Aggregation)` |
| `base_model` | `BC-pretrained MaskablePPO` |
| `observation_channels` | `15` |
| `board_size` | `10x20` |
| `expert` | `Monte Carlo solver (1000 samples)` |
| `disagree_only` | `True` |
| `confidence_threshold` | `0.3` |
| `freeze_cnn` | `True` |

## Evaluation Results

| Metric | Value |
|--------|-------|
| mean_shots_500_games | 100.90 |
| verified_games | 500 |
| seed | 20000 |
| notes | 15-channel DAgger; first model to break 100-shot barrier |

## Usage

```python
from training.hub import load_model_from_hub

model = load_model_from_hub("Hanks1234/battleship-ppo-dagger")
```