Code for simulations in CDC'22 paper "A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems" by L. Ballotta, G. Peserico, and F. Zanini (folder "CDC paper") and in IEEE TNSE article "To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing" by L. Ballotta, G. Peserico, F. Zanini, and P. Dini (folder "Journal paper").
CDC paper:
- to train a Q-learning sensing policy, set desired parameters in params_hom and run qlearning;
- to test and plot behavior of pre-trained and custom policies, run test_custom_policies and compare_policies.
Journal paper:
- Homogeneous sensors: same as CDC paper.
- Heterogeneous sensors:
-
To train a Q-learnig policy with new data:
- copy files in "udpapp" folder and paste them in your omnet++ udpapp $your_omnet_dir\samples\inet\src\inet\applications\udpapp;
- copy "MLForWirelessProject_eter2" folder and paste it in your omnet++ wireless example $your_omnet_dir\samples\inet\examples\wireless.
-
To open OMNeT++:
- open folder $your_omnet_dir\samples\inet\examples\wireless\MLForWirelessProject_eter2;
- run omnetpp.ini.
-
To choose whether to train a policy from scratch or to use a pre-trained policy, you will want to set the following parameters in file UdpEchoAppMLfW_eter2.cc:
To train a Q-learnig policy with new data, run qlearning.bool usepolicy_e = true; //to use pre-trained policy bool singleEpisode = true; // to run a single episode (to evaluate the policy) int policy_e[5] = {89,89,88,71,70}; //to force a custom policy -
Training data are automatically saved in the folder "experiments".
- CDC paper: https://arxiv.org/abs/2204.00703__
Please cite as: L. Ballotta, G. Peserico, F. Zanini, "A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems," Proceedings of the IEEE Conference on Decision and Control (CDC), 2022. - Journal article: https://arxiv.org/abs/2209.02166__
Please cite as: L. Ballotta, G. Peserico, F. Zanini, P. Dini, "To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing," in IEEE Transactions on Network Science and Engineering, 2023.