simillusion/iris-atari-boxing

reinforcement-learning model by simillusion

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A demo checkpoint from Simulusion, an open-source PyTorch library that puts world models behind one API. It implements the paper Transformers are Sample-Efficient World Models (Micheli et al., ICLR 2023). IRIS compresses every frame into 16 discrete tokens with a VQ autoencoder, models the game as a sequence of tokens and actions with a Transformer, and trains its policy only on the Transformer's imagination. Trained for 300 epochs (60,000 real environment steps) on a 4 GB RTX 3050 laptop GPU. The world model keeps the ring, both boxers and the scoreboard, and the policy has learned to close in and trade punches. In a full round in the real emulator it led 29-21 with 24 seconds left and lost 31-32: level with the game's built-in opponent, not yet better. The paper reaches about +70 with several times more policy training per…

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Model overview

simillusion/iris-atari-boxing is a reinforcement-learning model repository published by simillusion on Hugging Face. The source reports the simulusion library.

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Model facts

Task: reinforcement-learning
Library: simulusion
Recent downloads (30 days): 0
Cumulative likes: 0
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: mit
Access: Not gated by Hugging Face
Created: 2026-10-27T06:13:54.000Z
Last modified: 2026-10-27T06:14:25.000Z

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