arXiv Artificial Intelligence

Efficient Diversity-based Experience Replay for Deep Reinforcement Learning

1 hour ago 11.09.2026 07:00 👁 1 views
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning

Quick summary

arXiv:2410.20487v5 Announce Type: replace-cross Abstract: Experience replay is widely used to improve learning efficiency in reinforcement learning by leveraging past experiences. However, existing experience replay methods, whether based on uniform or prioritized sampling, often suffer from low efficiency, particularly in real-world scenarios with high-dimensional state spaces. To address this limitation, we propose a novel approach, Efficient Diversity-based Experience Replay (EDER). EDER employs a determinantal point process to model the diversity between samples and prioritizes replay base

Key takeaways

  • arXiv:2410.20487v5 Announce Type: replace-cross Abstract: Experience replay is widely used to improve learning efficiency in reinforcement learning by leveraging past experiences.
  • However, existing experience replay methods, whether based on uniform or prioritized sampling, often suffer from low efficiency, particularly in real-world scenarios with high-dimensional state spaces.
  • To address this limitation, we propose a novel approach, Efficient Diversity-based Experience Replay (EDER).

Why it matters

This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗