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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).
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