JRC-AoI

Supplementary material for the following papers:

1. J. Lee, D. Niyato, Y. L. Guan and D. I. Kim, "Learning to Schedule Joint Radar-Communication With Deep Multi-Agent Reinforcement Learning," in IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 406-422, Jan. 2022, doi: 10.1109/TVT.2021.3124810.

2. J. Lee, D. Niyato, Y. L. Guan and D. I. Kim, "Learning to Schedule Joint Radar-Communication Requests for Optimal Information Freshness," 2021 IEEE Intelligent Vehicles Symposium (IV), 2021, pp. 8-15, doi: 10.1109/IV48863.2021.9575131.

   Also available on Digital Repository of NTU.

Getting started

Install the dependencies listed in 'requirements.txt'.

Running Experiments

The DQN training process, as well as the 1-step planning and round robin baseline algorithms, may be run from the command line. Examples are provided below. DQN:

python dqn_JRC_AoI_d.py --obj avg --rd_bad2b 0.1 0.2 0.1 --w_radar 9 10 1 --w_ovf 1 --data_gen 2 3 1 --nn_size 64 64 --double --dueling --n_experiments 5

1-Step Planner:

python test_JRC_AoI2b.py --obj avg --mode best --rd_bad2b 0.1 0.2 0.1 --w_radar 9 10 1 --data_gen 2 3 1 --n_experiments 5

Round Robin:

python test_JRC_AoI2b.py --obj avg --mode rotate --rd_bad2b 0.1 0.2 0.1 --w_radar 9 10 1 --data_gen 2 3 1 --n_experiments 5