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🔗 Source: proceedings.mlr.press
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👤 Author: Anonymous Submission
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Researchers propose a method for learning agents with quality and diversity in reinforcement learning by harnessing distribution ratio estimators. Quality-Diversity (QD) algorithms generate diverse behavioral niches while ensuring high quality within each niche. The approach builds on Stein variational gradient descent and utilizes kernels based on f-divergence between policy stationary distributions. Distribution ratio estimators, such as DualDICE, ValueDICE, and GenDICE, are used to estimate the ratio of stationary distributions, enabling the computation of gradients for policies in an ensemble. Experiments demonstrate the effectiveness of this approach in generating diverse behaviors with high returns, particularly in tasks with deceptive rewards or lacking external reward signals.

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Researchers propose a method for learning agents with quality and diversity in reinforcement learning by harnessing distribution ratio estimators.

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  • File Format: PDF
  • File Size: 1005 KB
  • Pages: 10
  • Language: EN
  • Author: Anonymous Submission
  • Total Downloads: 63
  • Last Updated: 13 hours ago

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