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license: mit
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<h3 align="center">
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<a href="https://github.com/DanHrmti/HECRL">Code</a> •
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<a href="https://taldatech.github.io/">Tal Daniel</a>, <a href="https://amyzhang.github.io/">Amy Zhang</a>, <a href="https://avivt.github.io/avivt/">Aviv Tamar</a> and <a href="https://cs.brown.edu/people/gdk/">George Konidaris</a>.
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Datasets contain 128x128 RGB images, state information, actions, and information for calculating goal-conditioned rewards.
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For RL training on image-based tasks, images in the dataset are preprocessed into latent representations using [visual encoders](https://huggingface.co/DanHrmti/hecrl_visual_encoders) which were pretrained on the same dataset.
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## Citation
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license: mit
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<h1 align="center">Hierarchical Entity-Centric Reinforcement Learning (HECRL)</h1>
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<h1 align="center">ICLR 2026</h1>
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<h3 align="center">
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<a href="https://github.com/DanHrmti/HECRL">Code</a> •
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<a href="https://taldatech.github.io/">Tal Daniel</a>, <a href="https://amyzhang.github.io/">Amy Zhang</a>, <a href="https://avivt.github.io/avivt/">Aviv Tamar</a> and <a href="https://cs.brown.edu/people/gdk/">George Konidaris</a>.
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Datasets contain 128x128 RGB images, state information, actions, and information for calculating goal-conditioned rewards.
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For RL training on image-based tasks, images in the dataset are preprocessed into latent representations using [visual encoders](https://huggingface.co/DanHrmti/hecrl_visual_encoders) which were pretrained on the same dataset.
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## Citation
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