env.py can create the environment. DQN.py is the function code of DQN. train_DQN.py train the DQN model. test_DQN.py test the DQN model. pdqn.py is the function code of PQDN, and it can test the PDQN model. train_PDQN.py train the PDQN model.
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power-allocation-and-user-device-association-with-deep-reinforcement-learning's Issues
bug
Hello chikaihsieh, thank you very much for your open source, which is very helpful to me, can you provide memory.py?
some confusion
Thank you so much for sharing this code so generously. I run this code successfully, but I have some confusion.
1 Why only SBS is considered when considering power consumption instead of the whole system, includingMBS. 2in the simulation results, what does Qos bad,Qos good and Qos grantee mean
bug
paper
Excuse me, is there a paper on this code?
No module named ‘memory’
There’s someting is missed here! Is this file named memory.memory? How I can slove this?
Which file can I run to draw these figures ?
Which file can I run to draw these figures ?
Are you sure there’s no files are missed!
Originally posted by @sanabel99 in #3 (comment)
Figures
Hello, thank you for good documents.
How I can draw the followinig figures
- Average R vs time steps for PDQN with/ without cluster constrain.
- Reward & enegery effecient vs steps for all problems ( P-DQN…ect)
- and those figures for average interference!
Im waiting for your answer.
Best,
Missed files
Im looking to file that draw these figures !
Im looking to file that draw these figures !
missing utils.py
Hello!!
I think you're missing the utils.py. I'm getting "ModuleNotFoundError: No module named 'utils'"
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