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mischievers/
openfront-rl-multiagent

reinforcement-learning

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OpenFront RL Agent

PPO-trained agent for 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.

mischievers/openfront-rl-multiagent

OpenFront RL Agent

Training Details

Final Training Metrics

Usage

Repository

mischievers/
openfront-rl-multiagent