tinywmcraft_200k

~200k pixel transitions from Craftax-Classic (Craftax-Classic-Pixels-v1), collected by a frozen pretrained agent (greedy actions, mid-game skill, mean episode ≈ 292 steps). This is the fixed data budget of the tinywmcraft world-model benchmark: train a pixel world model from scratch on train.npz in ≤ 10 min on one A100 and score it on eval.npz with the repo's frozen metric (rFVD + action-following).

Files

file	frames	actions / rewards / dones	envs
train.npz	(1955, 102, 63, 63, 3) u8	(1954, 102)	102
eval.npz	(401, 128, 63, 63, 3) u8	(400, 128) (no rewards)	128

Layout is time-major: frames[t, e] is the frame of env e at step t, and actions[t, e] is the action taken from frames[t, e] that produced frames[t+1, e]. dones[t, e] = True means the episode ended on that step — frames[t+1, e] is the first frame of a fresh episode (reset-on-done, so transitions span all episode positions). Each file also carries a meta key (json string) with collection details.

The eval envs use seeds disjoint from the train envs. rewards are included in the train split as a bonus (reward-conditioned world models, offline RL, ...) — the benchmark itself does not use them.

Loading

import numpy as np
d = np.load("train.npz")
frames, actions, dones = d["frames"], d["actions"], d["dones"]

License

MIT, like the benchmark repo. Craftax itself is MIT-licensed.