Intelligent Game-Theoretic Deep Reinforcement Learning Caching for Named Data Networking: A Unified Optimization Framework

Abstract

Named Data Networking (NDN) revolutionizes content-centric communication through intrinsic in-network caching, yet optimal cache management remains challenging under dynamic content popularity and resource constraints. This paper introduces GT-DRLCache, a novel framework that synergistically integrates non-cooperative game theory with multi-agent deep reinforcement learning (MADRL) for intelligent caching decisions. We formulate the distributed caching problem as a stochastic congestion game with provable Nash equilibrium existence. Each router employs a hybrid Double-DQN/Proximal Policy Optimization (PPO) agent that learns equilibrium-aware caching strategies while adapting to temporal traffic patterns. Extensive simulations using ndnSIM 2.8 on GEANT, Abilene, and synthetic Barabási-Albert topologies demonstrate consistent performance improvements: 32–56% higher hit ratios, 28–44% latency reduction, and up to 50% upstream traffic savings compared to LRU, LFU, MPC, ProbCache, and DRL-only baselines. This work provides comprehensive theoretical foundations, complete algorithmic implementation, and thorough experimental validation of the proposed unified caching framework.