simillusion/dreamer-dmc-cartpole-swingup

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 Dream to Control: Learning Behaviors by Latent Imagination (Hafner et al., ICLR 2020). Dreamer learns a recurrent state-space model of the environment from pixels, then trains an actor-critic purely on imagined trajectories. Trained from scratch for 60k environment steps (about 95 minutes on a 4 GB RTX 3050 laptop GPU). Evaluation return rose from about 75 to 493, out of a maximum of roughly 880: the policy swings the pole up but cannot yet balance it. The world model tracks the real cart for about ten imagined steps before it drifts. This is a short demo run sized for a laptop GPU,…

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Model overview

simillusion/dreamer-dmc-cartpole-swingup 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: 2026-10-27T06:12:47.000Z
Last modified: 2026-10-27T06:13:23.000Z

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