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1603.00748.pdf
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🔗 Source: arxiv.org
📊 Size: 1.63 MB
📄 Pages: 13 pages
⬇️ Downloads: 294

Summary

Researchers propose two techniques to improve the efficiency of deep reinforcement learning in continuous control domains. The first technique is a continuous variant of the Q-learning algorithm, called normalized advantage functions (NAF), which allows for Q-learning with experience replay in continuous tasks and improves performance on simulated robotic control tasks. The second technique combines NAF with learned models to accelerate learning, using iteratively refitted local linear models and on-policy imagination rollouts to achieve substantial improvement in sample complexity. The method is evaluated on simulated robotic tasks and compared to prior methods, demonstrating its effectiveness in reducing sample complexity and improving learning efficiency.

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Researchers propose two techniques to improve the efficiency of deep reinforcement learning in continuous control domains.

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  • File Format: PDF
  • File Size: 1.63 MB
  • Pages: 13
  • Language: EN
  • Total Downloads: 294
  • Last Updated: 12 hours ago

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