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iwillsolvehardestproblem/dreamerv3-atari100k-breakout
dreamerv3-atari100k-breakout is a reinforcement learning model from iwillsolvehardestproblem. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
To our knowledge the first publicly released DreamerV3 Atari checkpoint. Trained for the dream-drift rollout-drift benchmark.
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Updated Aug 26, 2026
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From the Hugging Face model README
To our knowledge the first publicly released DreamerV3 Atari checkpoint. Trained for the dream-drift rollout-drift benchmark.
--configs atari100k --task atari100k_breakouttrain_Breakout.log)ckpt/<snapshot>/agent.pkl (full agent: world model + policy, ~2 GB), ckpt/latest pointer, breakout_dreamer_replay.npz (last ~7k steps of its own replay: obs uint8 (N,64,64,3), act, rew, end, ep_len, num_actions=4), training log.# inside a danijar/dreamerv3 checkout with its requirements installed
# point --logdir at a folder containing this repo's ckpt/ directory, or see
# harness/dreamer_drift.py in the dream-drift repo for a scoring example.
Open-loop 50-step dreams vs. ground truth, 32 starts x 4 samples (protocol in the dream-drift repo): in-distribution k1 MSE 1.2e-3, k50 1.7e-3 (drift 5.3e-4); on expert-policy states 7x worse with narrower sample spread (the "confident healing" failure mode).