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Double Q-learning.pdf

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Preview of Double Q-learning
🔗 Source: papers.nips.cc
📊 Size: 190 KB
👤 Author: Hado V. Hasselt
⬇️ Downloads: 98

Summary

Double Q-learning addresses Q-learning's poor performance in stochastic environments due to large overestimations of action values, caused by using the maximum action value as an approximation for the maximum expected action value. An alternative double estimator method is introduced to approximate the maximum expected value, which sometimes underestimates rather than overestimates. Double Q-learning is a new off-policy reinforcement learning algorithm that converges to the optimal policy and performs well in settings where Q-learning performs poorly.

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Description

Double Q-learning addresses Q-learning's poor performance in stochastic environments due to large overestimations of action values, caused by using the maximum...

Technical Information

  • File Format: PDF
  • File Size: 190 KB
  • Pages: 9
  • Language: EN
  • Author: Hado V. Hasselt
  • Total Downloads: 98
  • Last Updated: 3 weeks ago

Document Overview

This PDF document about Double Q-learning provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Double Q-learning.

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If you're interested in Double Q-learning, you might also want to explore:

Double guideDouble tutorialQ-learning guideQ-learning tutorial

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