Distributed Q-Learning Routing: A Pragmatic Approach to Sparse Graphs

Distributed Q-Learning routing offers a memory-efficient alternative to monolithic pathfinding in sparse graphs. By distributing intelligence across independent nodes, each agent learns the best local action to reach a global target. This senior dev’s guide explores the Q-Learning update rule, implementation logic, and why distributed agents outperform traditional N³ matrix approaches.