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README.md

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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:** 20
+- **Global steps:** 57109
+- **Best mean reward:** 151.78631000000004
+
+## Final Training Metrics
+
+- **Mean reward:** 151.78631000000004
+- **Mean episode length:** 2855.45
+- **Loss:** 4.396657466888428
+
+## Usage
+
+```python
+from train import ActorCritic
+import torch
+
+model = ActorCritic(obs_dim=32, max_neighbors=8)
+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).