OpenFront spatial autoencoder
Frozen spatial observation encoder for OpenFront.io RL agents: compresses tile ownership + terrain + fallout + static structures into a latent grid (any map size). Structure detection is at precision/recall 1.0 per class for every model below.
Current best: ae_v31_d8.pt — v3.1 architecture (terrain-conditioned
nearest-upsample decoder, focal loss, border-dense sampling, cosine LR) with
the latent at 1/8 resolution instead of 1/16. Trained 40k steps on a mixed
corpus of bot self-play and real archived human games.
Border-tile accuracy (the hard metric — overall tile accuracy saturates at ~99% for all models), uniform 256-crop eval:
| checkpoint | latent | human border | bot border | human tiles | bot tiles |
|---|---|---|---|---|---|
ae_v31_d8.pt | 64ch @ 1/8 | 89.3% | 96.1% | 99.6% | 99.8% |
ae_v31.pt | 64ch @ 1/16 | 78.5% | 90.7% | 99.1% | 99.6% |
ae_v3.pt (bot-only) | 64ch @ 1/16 | 71.8% | 87.5% | 99.2% | 99.4% |
The ablation story: mixing human games into training fixes the human-domain gap, the v3.1 decoder/loss fixes add a few points, and latent resolution (not channel count) is what finally cracks border geometry — borders are high-frequency spatial detail that can't be bought back with more channels.
Full details, training code, results, and roadmap: github.com/djmango/openfront-ai
Trained on djmango/openfront-snapshots (bot) and djmango/openfront-human-games (human).
import torch
from ae.model_v3 import SpatialAE
ckpt = torch.load("ae_v31_d8.pt", map_location="cpu", weights_only=False)
a = ckpt["args"]
model = SpatialAE(
latent_c=a["latent_c"],
terrain_cond=a.get("terrain_cond", False),
upsample_decoder=a.get("upsample_decoder", False),
latent_down=a.get("latent_down", 16),
)
model.load_state_dict(ckpt["model_state_dict"])
model.eval()
z = model.encode(owner_slots, terrain, static_planes) # (B, 64, H/8, W/8)
ae.pt (v1 tile-only TileAutoencoder in model.py) is kept for history.