mlsanityData to 14 Oct 2026

Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning

arXiv 2507.05900 ↗First posted 8 Jul 2025cs.SD cs.LG eess.AScited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This study addresses the problem of stable acoustic relay assignment in an underwater acoustic network. Unlike the objectives of most existing literature, two distinct objectives, namely classical stable arrangement and ambiguous stable arrangement, are considered. To achieve these stable arrangements, a laser chaos-based multi-processing learning (LC-ML) method is introduced to efficiently obtain high throughput and rapidly attain stability. In order to sufficiently explore the relay's decision-making, this method uses random numbers generated by laser chaos to learn the assignment of relays to multiple source nodes. This study finds that the laser chaos-based random number and multi-processing in the exchange process have a positive effect on higher throughput and strong adaptability with environmental changing over time. Meanwhile, ambiguous cognitions result in the stable configuration with less volatility compared to accurate ones. This provides a practical and useful method and can be the basis for relay selection in complex underwater environments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 37 references that are in the map. First posted on arXiv 8 Jul 2025.

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5 of 37 references.

References · 5

  1. 2017Ultrafast photonic reinforcement learning based on laser chaoscited by 13
  2. 2014Efficient Decision-Making by Volume-Conserving Physical Objectcited by 11
  3. 2018Scalable photonic reinforcement learning by time-division multiplexing of laser chaoscited by 8
  4. 2019Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time seriescited by 7
  5. 2020Arm order recognition in multi-armed bandit problem with laser chaos time seriescited by 2

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  5. 2026Multi-AUV Trajectory Learning for Sustainable Underwater IoT with Acoustic Energy Transfer ↗
  6. 2025When Motion Learns to Listen: Diffusion-Prior Lyapunov Actor-Critic Framework with LLM Guidance for Stable and Robust AUV Control in Underwater Tasks
  7. 2026Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach
  8. 2024Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network