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Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving

Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving

Wensheng Su
Zhenni Li
Minrui Xu
Jiawen Kang
Dusit Niyato
Shengli Xie
ArXiv (abs)PDFHTML

Papers citing "Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving"

3 / 3 papers shown
Title
TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration
TinyMA-IEI-PPO: Exploration Incentive-Driven Multi-Agent DRL with Self-Adaptive Pruning for Vehicular Embodied AI Agent Twins Migration
Zhuoqi Zeng
Yuxiang Wei
Jiawen Kang
95
0
0
30 Apr 2025
Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems
Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems
Jinbo Wen
Jiawen Kang
Dusit Niyato
Yang Zhang
Shiwen Mao
31
7
0
02 Aug 2024
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models
Heng Lu
Mehdi Alemi
Reza Rawassizadeh
95
1
0
05 Jul 2024

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