---
title: "PRIME：多智能体环境中无人机辅助应急通信网络的塑性恢复方法"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.17922v1"
permalink: "https://feed.astralor.com/items/itm_b7c6e8d7d7044153246ef00b"
published: "2026-07-20T13:19:58.000Z"
score: 76
channel: "tech"
tags: ["PRIME","塑性恢复","多智能体强化学习","无人机通信","沉默神经元"]
---

# PRIME：多智能体环境中无人机辅助应急通信网络的塑性恢复方法

## AI 点评

> 这篇论文提出的PRIME方法巧妙地利用了梯度信号而非人工代理来判断神经元活性，避免了多智能体场景中暴力重置带来的训练不稳定。对于从事分布式AI系统、机器人调度的研究人员，它将持续学习与多智能体学习的交叉点推进了一步，理论界也保证了扩展性，值得关注。

## 速览

大多数针对无人机应急通信网络的强化学习控制器假设环境平稳，少数能处理变化的也只关注外部影响而忽略网络内部状态。本文发现持续的非平稳性会直接损害内部状态：目标变化导致神经元逐渐休眠，共享策略失去学习能力。简单重置休眠神经元在多智能体共享参数训练中不安全，因为许多看似不活跃的神经元仍接收强梯度，且休眠与否取决于观测数据。PRIME通过双向沉默神经元框架，在合作多智能体强化学习中聚合全团队的激活与梯度统计，只重置同时激活休眠和梯度沉默的神经元，保留有用表征并恢复学习能力。在相切换无人机应急通信模拟器中，PRIME相比MAPPO将四分位均值回报提升24.9%，休眠神经元比例从40-45%降至10-20%，且动态遗憾界表明扰动成本与沉默子空间维度成比例。

## 关键点

- PRIME通过双向判定（激活休眠+梯度沉默）选择性重置神经元，避免暴力重置带来的安全性问题。
- 在无人机应急通信模拟器中，PRIME将休眠神经元比例从40-45%降至10-20%，四分位均值回报提升24.9%。
- 动态遗憾界证明扰动成本仅与沉默子空间维度成比例，而非全参数数量，理论保证了扩展性。

## 全文

Most reinforcement learning controllers for these networks assume stationary conditions, and the few that handle change react to the external environment while leaving the network's internal state unexamined. We show that sustained non-stationarity damages this internal state directly: as objectives shift, neurons progressively fall dormant and the shared policy loses the capacity to learn. The obvious remedy, resetting dormant neurons, is unsafe under shared-parameter multi-agent training: many neurons that appear inactive are still receiving strong training gradients, and whether a neuron appears dormant depends on which agent's observations it processes. PRIME (Plasticity Recovery In Multi-agent Environments) therefore verifies both directions before intervening. Extending the bidirectional Silent Neuron framework to cooperative multi-agent reinforcement learning, it aggregates activation and gradient statistics over the full team batch, reads the backward signal from the gradient the training loss has already deposited , not from a hand-crafted proxy, and reinitializes only neurons that are simultaneously activation-dormant and gradient-silent. Useful representations are preserved while learning capacity is restored. On a phase-switching UAV emergency communication simulator, PRIME improves interquartile mean return by 24.9\% over MAPPO and holds dormant neuron fractions at 10--20\% versus 40--45\%; ablations attribute the gains to the gradient signal and team-level aggregation rather than to the specific reset operator. A dynamic regret bound shows that the perturbation cost scales with the small silent-subspace dimension rather than the full parameter count.

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