---
license: mit
tags:
  - reinforcement-learning
  - ppo
  - openfront
  - game-ai
---

# OpenFront RL Agent

PPO-trained agent for [OpenFront.io](https://openfront.io), a multiplayer territory control game.

## Training Details

- **Algorithm:** PPO (Proximal Policy Optimization)
- **Architecture:** Actor-Critic with shared backbone (256→256→128)
- **Map:** world
- **Opponents:** 5 bots
- **Episodes trained:** N/A
- **Global steps:** 1536000
- **Best mean reward:** 122.21637367248535

## Final Training Metrics

- **Mean reward:** 102.8225530385971
- **Mean episode length:** 3839.8
- **Loss:** -0.008329648524522781

## Usage

```python
from train import ActorCritic
import torch

model = ActorCritic(obs_dim=78, max_neighbors=16)
model.load_state_dict(torch.load("best_model.pt", weights_only=True))
model.eval()
```

## Repository

Trained from [josh-freeman/openfront-rl](https://github.com/josh-freeman/openfront-rl).