rikonenobais/deep-q-learning-for-mobile-edge-computing

This repository implements a research simulator and Deep-Q-learning workflow for mobile-edge task offloading. A training script creates an Offload environment and per-device DQN agents, runs episodes, and collects reward, cost, delay, and task statistics. The environment models local execution, transmission, fog execution, queues, and delay limits; the agent uses recurrent state and experience replay to select actions and learn. The README identifies train.py as the main entry point; trainPaperParam.py is a parallel experiment script. Excerpts omit some method calls, so internal environment wiring is shown only where directly supported.