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🔗 Source: proceedings.mlr.press
📊 Size: 8.1 MB
👤 Author: Jaekyeom Kim, Seohong Park, Gunhee Kim
⬇️ Downloads: 158

Summary

Unsupervised skill discovery is a problem where an agent learns inherent skills from environments without external rewards or supervision. The proposed method, Information Bottleneck Option Learning (IBOL), uses a linearizer to simplify environment dynamics and an information bottleneck framework to learn skills in a disentangled and interpretable way. IBOL outperforms state-of-the-art methods in multiple MuJoCo environments, including Ant, HalfCheetah, Hopper, and D'Kitty, in terms of information-theoretic evaluations and downstream tasks. The method's key contributions include separating the problem of making transitions in the state space from skill discovery, proposing a novel skill discovery method with information bottleneck, and showing superior performance to existing methods.

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Unsupervised skill discovery is a problem where an agent learns inherent skills from environments without external rewards or supervision.

Technical Information

  • File Format: PDF
  • File Size: 8.1 MB
  • Pages: 11
  • Language: EN
  • Author: Jaekyeom Kim, Seohong Park, Gunhee Kim
  • Total Downloads: 158
  • Last Updated: 3 hours ago

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