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Stochastic Planning Inference.pdf

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Preview of Stochastic Planning Inference
🔗 Source: proceedings.mlr.press
📊 Size: 548 KB
📄 Pages: 12 pages
⬇️ Downloads: 412

Summary

Stochastic planning is reduced to probabilistic inference in large discrete graphical models, requiring approximation schemes due to hardness of inference. A new categorization unifies prior work along two dimensions: direction of information flow (forward vs. backward inference) and type of approximation (e.g., Belief Propagation vs. mean field variational inference). Experimental evaluation shows forward Belief Propagation outperforms several mean field variational inference algorithms, and a novel algorithm, Collapsed State Variational Inference, provides a tighter approximation and comparable planning performance.

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Description

Stochastic planning is reduced to probabilistic inference in large discrete graphical models, requiring approximation schemes due to hardness of inference.

Technical Information

  • File Format: PDF
  • File Size: 548 KB
  • Pages: 12
  • Language: EN
  • Total Downloads: 412
  • Last Updated: 1 week ago

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