MEC

Deep Q Learning:

Here are some good DQN Python codes. They are all free to be downloaded. You need to use Python > 3.7 and Tensorflow >2.0 to run them.

  1. https://github.com/anita-hu/TF2-RL
  2. https://github.com/chagmgang/tf2.0_reinforcement_learning
  3. https://github.com/marload/DeepRL-TensorFlow2

Deep Reinforcement Learning:

Here are some good DQN Python codes. They are all free to be downloaded. You need to use Python > 3.7 and Tensorflow >2.0 to run them.

  1. https://github.com/anita-hu/TF2-RL
  2. https://github.com/chagmgang/tf2.0_reinforcement_learning
  3. https://github.com/marload/DeepRL-TensorFlow2

Mobile Edge Computing Publications:

  1. L. Zhang, B. Jabbari and N. Ansari, “Deep Reinforcement Learning Driven UAV-assisted Edge Computing,” IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25449-25459, Dec. 2022.
  2. L. Zhang and B. Jabbari, “Machine Learning Driven Latency Optimization for Application-aware Edge Computing-based IoTs,” Proc. IEEE International Conference on Communications (ICC), pp. 183-188, May 2022.
  3. L. Zhang, B. Jabbari and N. Ansari, “Machine Learning Driven UAV-assisted Edge Computing,” Proc. IEEE Wireless Communications and Networking Conference (WCNC), pp. 2220-2225, Apr. 2022.
  4. L. Zhang and J. Chakareski, “UAV-Assisted Edge Computing and Streaming for Wireless Virtual Reality,” accepted for publication in IEEE Transactions on Vehicular Technology, Jan. 2022.
  5. L. Zhang and N. Ansari, “Optimizing the Operation Cost for UAV-aided Mobile Edge Computing,” IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 6085-6093, Jun. 2021.
  6. L. Zhang and N. Ansari, “Latency-Aware IoT Service Provisioning in UAV-Aided Mobile-Edge Computing Networks,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 10573-10580, Oct. 2020.