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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 (512→512→256)
- **Observation dim:** 96
- **Max neighbors:** 16
- **Maps:** plains, big_plains, ocean_and_land, half_land_half_ocean (random per episode)
- **Opponents:** N/A Easy bots
- **Parallel envs:** 16
- **Learning rate:** 0.00034
- **Rollout steps:** 1024
- **Updates trained:** 660
- **Global steps:** 86507520
- **Best mean reward:** -0.06284408122301102
## Final Training Metrics
- **Mean reward:** -0.5554914677888155
- **Mean episode length:** 7626.04
- **Loss:** -0.16370002925395966
## Usage
```python
from train import ActorCritic
import torch
model = ActorCritic(obs_dim=96, max_neighbors=16, hidden_sizes=[512, 512, 256])
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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