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Nested Mixture Of Experts For Hybrid Dynamical Systems.pdf

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Preview of Nested Mixture of Experts for Hybrid Dynamical Systems
🔗 Source: proceedings.mlr.press
📊 Size: 952 KB
📄 Pages: 12 pages
⬇️ Downloads: 143

Summary

Cooperative and Competitive Learning of Hybrid Dynamical Systems" by Junhyeok Ahn and Luis Sentis presents a novel method for representing and learning hybrid dynamical systems using a nested mixture of experts (NMOE) model. Key points include:

- Problem: Model-based reinforcement learning (MBRL) algorithms struggle with sample efficiency and incorporating domain knowledge, especially for complex hybrid systems.
- Solution: NMOE combines white-box (analytic) and black-box (neural network) models, optimizing the bias-variance trade-off. It provides a structured way to incorporate prior knowledge by training local experts cooperatively or competitively.
- Prior Knowledge: Includes information about robots' physical contacts with the environment and their kinematic and dynamic properties.
- Demonstration: The authors show the effectiveness of NMOE in various continuous control domains, including hybrid dynamical systems, in terms of data efficiency, generalization, and bias-variance trade-off.
- Evaluation: The NMOE is evaluated in an MBRL setup, integrated with a model-based controller and trained online.
- Keywords: Learning of Hybrid Dynamical Systems, System Identification, Model-based Control.

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Description

Nested Mixture of Experts' learns hybrid dynamical systems, balancing bias-variance and incorporating domain knowledge.

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  • File Format: PDF
  • File Size: 952 KB
  • Pages: 12
  • Language: EN
  • Total Downloads: 143
  • Last Updated: 4 hours ago

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