mlsanityData to 14 Oct 2026

PER-DPP Sampling Framework and Its Application in Path Planning

arXiv 2503.07411 ↗First posted 10 Mar 2025cs.RO cs.LG math.OCcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Autonomous navigation in intelligent mobile systems represents a core research focus within artificial intelligence-driven robotics. Contemporary path planning approaches face constraints in dynamic environmental responsiveness and multi-objective task scalability, limiting their capacity to address growing intelligent operation requirements. Decision-centric reinforcement learning frameworks, capitalizing on their unique strengths in adaptive environmental interaction and self-optimization, have gained prominence in advanced control system research. This investigation introduces methodological improvements to address sample homogeneity challenges in reinforcement learning experience replay mechanisms. By incorporating determinant point processes (DPP) for diversity assessment, we develop a dual-criteria sampling framework with adaptive selection protocols. This approach resolves representation bias in conventional prioritized experience replay (PER) systems while preserving algorithmic interoperability, offering improved decision optimization for dynamic operational scenarios. Key contributions comprise: Develop a hybrid sampling paradigm (PER-DPP) combining priority sequencing with diversity maximization.Based on this,create an integrated optimization scheme (PER-DPP-Elastic DQN) merging diversity-aware sampling with adaptive step-size regulation. Comparative simulations in 2D navigation scenarios demonstrate that the elastic step-size component temporarily delays initial convergence speed but synergistically enhances final-stage optimization with PER-DPP integration. The synthesized method generates navigation paths with optimized length efficiency and directional stability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 9 references that are in the map. First posted on arXiv 10 Mar 2025.

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5 of 9 references.

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  1. 2024Efficient Diversity-based Experience Replay for Deep Reinforcement Learning27 Oct

Other references · 4

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2015Prioritized Experience Replaycited by 1,099
  3. 2020Revisiting Fundamentals of Experience Replaycited by 175
  4. 2022Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworkscited by 5

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  8. 2025Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments