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knights_archers_zombies_v10)
and predicts a 28-dim game state (archer/knight positions + up to 10 zombies) from a
frozen embedding. Pixels in, state out — the model reads no RAM at inference time.kaz_ma_128x128.lance):| File | Rollout steps | Warmup | Notes |
|---|---|---|---|
kaz_R1.pt | 1 | – | single-step baseline |
kaz_R3.pt | 3 | 10 | discount 0.9 |
kaz_R5.pt | 5 | 15 | discount 0.9 |
game, state_dim=28,
state_names, state_mean/std, num_actions=6, and embed_dim.1import torch
2ckpt = torch.load("kaz_R5.pt", map_location="cpu", weights_only=False)
3print(ckpt["game"], ckpt["state_dim"], ckpt["state_names"])python -m server.play --game kaz --checkpoint checkpoints/kaz_R5.pt