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tztsai / Energy-Efficient-5G-RL

This repository presents a multi-agent reinforcement learning approach for energy-efficient collaborative control of base stations in 5G networks.
MIT License
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Where is the 'sim_states' & 'results\MultiCellNetwork\RANDOM\mappo\check'folder? #8

Closed Haochi1222 closed 7 months ago

Haochi1222 commented 9 months ago

Hello, When I run the file, It suggests that No such file or directory: 'sim_stats\mappo\C\bs_stats.csv' & No such file or directory: 'logs/None_mappo_check.log' Could you please provide these folders? Thank you.

tztsai commented 9 months ago

Hello, you need train the agent first and then run the simulation using "simulate.py"

Haochi1222 commented 9 months ago

Hello, you need train the agent first and then run the simulation using "simulate.py"

Hello , Thank you for your reply,

微信图片_20240118004422

I successfully run the train_dqn.sh file， but when I run the train.sh, It suggests that " 'tuple' object cannot be interpreted as an integer. " Sorry I'm a beginner in reinforcement learning，Have you encountered similar problems when running code? Looking forward to your reply. Thank you very much again.

tztsai commented 9 months ago

Sorry it seems that I introduced some bug after I added the DQN agent. You can checkout the old branch which contains the working code when I published the paper.

tztsai commented 9 months ago

Hello, sorry for the late update, but I think the code in the main branch should also work now.