├── .idea ├── .gitignore ├── Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV.iml ├── Multihop-V2V.iml ├── deployment.xml ├── inspectionProfiles │ ├── Project_Default.xml │ └── profiles_settings.xml ├── misc.xml ├── modules.xml └── vcs.xml ├── README.md ├── env ├── __init__.py ├── config.py ├── datastruct.py ├── environment.py └── utils.py └── methods ├── A3C ├── a3c.py ├── outputs │ └── Multihop-V2V │ │ ├── 20230926-211815 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── config_params.txt │ │ │ ├── params.txt │ │ │ ├── record.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20230927-153132 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── config_params.txt │ │ │ ├── params.txt │ │ │ ├── record.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20230927-153927 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── config_params.txt │ │ │ ├── params.txt │ │ │ ├── record.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20230928-110901 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20230930-114045 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231002-214935 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── config_params.txt │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231004-150720 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231004-162835 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231005-153812 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231005-192015 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── config_params.txt │ │ │ ├── params.txt │ │ │ ├── record.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231008-130030 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231011-143350 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231011-185838 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231012-213018 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231013-085149 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231013-145006 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231013-170138 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231013-203310 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231017-090344 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231025-084607 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231107-153511 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ └── params.txt │ │ ├── 20231115-085643 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231120-093833 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231122-103437 │ │ ├── models │ │ │ └── a3c.pt │ │ └── results │ │ │ ├── params.txt │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ └── 20231123-164321 │ │ ├── models │ │ └── a3c.pt │ │ └── results │ │ ├── params.txt │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps └── run_A3C.py ├── DQN ├── README.md ├── dqn.py ├── outputs │ └── Multihop-V2V │ │ ├── 20231009-101444 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231009-102149 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231009-102312 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231011-143243 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231013-084908 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231016-141744 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231018-102922 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231020-192110 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231023-092959 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231025-084607 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231115-085643 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ ├── 20231122-103436 │ │ └── results │ │ │ ├── train_completion_rate.npy │ │ │ ├── train_completion_rate_curve.eps │ │ │ ├── train_ma_completion_rate.npy │ │ │ ├── train_ma_rewards.npy │ │ │ ├── train_rewards.npy │ │ │ └── train_rewards_curve.eps │ │ └── 20231123-161552 │ │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps └── run_DQN.py ├── Draw_pictures ├── A3C_completion_rate.pdf ├── A3C_completion_rate_lr.pdf ├── A3C_rewards.pdf ├── A3C_rewards_lr.pdf ├── contrast_completion_rate.pdf ├── contrast_rewards.pdf ├── different_task_computation_resource_average_rewards.pdf ├── different_tasksize_average_rewards.pdf ├── different_vehicle_number_average_rewards.pdf ├── different_vehicle_speed_average_rewards.pdf └── draw.py └── Greedy ├── greedy_tasksize.py ├── outputs └── Multihop-V2V │ ├── 20231005-192011 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231008-130015 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231016-193257 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231020-215837 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231025-140726 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231025-152310 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231105-092657 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231105-150100 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231106-084841 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231106-143001 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231106-182637 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231107-103741 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231107-161928 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231108-082916 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231108-091912 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ ├── 20231108-102015 │ └── results │ │ ├── train_completion_rate.npy │ │ ├── train_completion_rate_curve.eps │ │ ├── train_ma_completion_rate.npy │ │ ├── train_ma_rewards.npy │ │ ├── train_rewards.npy │ │ └── train_rewards_curve.eps │ └── 20231115-085643 │ └── results │ ├── train_completion_rate.npy │ ├── train_completion_rate_curve.eps │ ├── train_ma_completion_rate.npy │ ├── train_ma_rewards.npy │ ├── train_rewards.npy │ └── train_rewards_curve.eps └── run_greedy.py /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | # Datasource local storage ignored files 5 | /dataSources/ 6 | /dataSources.local.xml 7 | # Editor-based HTTP Client requests 8 | /httpRequests/ 9 | -------------------------------------------------------------------------------- /.idea/Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/Multihop-V2V.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 12 | -------------------------------------------------------------------------------- /.idea/deployment.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 13 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 7 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV-Networks 2 | #This is the runnable Python project for the paper 'Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV-Networks'. 3 | 4 | './env/config' contains parameter configurations. 5 | 6 | './env/datastruct' contains the data struct of the project. 7 | 8 | './env/environment' contains environment settings. 9 | 10 | './methods/A3C' contains A3C algorithm. 11 | 12 | './methods/DQN' contains DQN algorithm. 13 | 14 | './methods/Greedy' contains Greedy algorithm. 15 | 16 | 17 | 18 | 19 | 20 | -------------------------------------------------------------------------------- /env/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZWLab23/Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV-Networks/8a3529dc3eeb6f7afd367288134456ac2f9740e6/env/__init__.py -------------------------------------------------------------------------------- /env/config.py: -------------------------------------------------------------------------------- 1 | import dataclasses 2 | # 旨在简化数据类的定义，减少样板代码，并提供更好的代码可读性。这对于处理大量数据对象的情况特别有用。 3 | import numpy as np 4 | 5 | 6 | @dataclasses.dataclass 7 | class VehicularEnvConfig: 8 | def __init__(self): 9 | # 道路信息 10 | self.road_range: int = 1200 # 道路长度 11 | self.road_width: int = 50 # 道路宽度 12 | 13 | # 时间信息 14 | self.time_slot_start: int = 0 15 | self.time_slot_end: int = 99 16 | 17 | # 任务信息相关（要处理的任务） 18 | self.Function_min_task_datasize=2# 19 | self.Function_max_task_datasize = 5 # 20 | self.Function_task_computing_resource: float = 300 # 任务计算资源300cycles/bit 21 | self.Function_min_task_delay: int = 20 # 任务的最小延迟20s 22 | self.Function_max_task_delay: int = 25 # 任务的最大延迟25s 23 | 24 | # 任务队列相关（每个卸载对象自己产生的任务，即自身到达任务） 25 | self.min_rsu_task_number: int = 2 #RSU最小任务个数 26 | self.max_rsu_task_number: int = 3 #RSU最大任务个数 27 | self.min_vehicle_task_number: int = 4 #车辆最小任务个数,用于生成初始任务的个数 28 | self.max_vehicle_task_number: int = 5 #车辆最大任务个数,用于生成初始任务的个数 29 | self.min_task_datasize: float = 2 # 2 MB 每个任务的最小数据大小 30 | self.max_task_datasize: float = 4 # 4 MB 每个任务的最大数据大小 31 | 32 | # 车辆相关 33 | self.min_vehicle_speed: int = 30 #车辆行驶的最小速度 34 | self.max_vehicle_speed: int = 40 #车辆行驶的最大速度 35 | self.min_vehicle_compute_ability: float =20000 #最小计算能力20000Mcycles/s 36 | self.max_vehicle_compute_ability: float =25000 #最大计算能力40000Mcycles/s 37 | self.vehicle_number = 10 #车辆个数 38 | self.seed = 1 #随机种子 39 | self.min_vehicle_y_initial_location:float =0 #y坐标最小值 40 | self.max_vehicle_y_initial_location: float =50 #y坐标最大值 41 | self.vehicle_x_initial_location:list=[0,self.road_range]#x坐标初始值 42 | # RSU相关 43 | self.rsu_number = 3 #RSU的个数 44 | self.min_rsu_compute_ability: float = 25000 # 最小计算能力25000Mcycles/s 45 | self.max_rsu_compute_ability: float = 30000 # 最大计算能力30000Mcycles/s 46 | # self._rsu_x_location: dict = {"rsu_1": 200, "rsu_2": 600, "rsu_3": 1000} 47 | # self._rsu_y_location: dict = {"rsu_1": 50, "rsu_2": 50, "rsu_3": 50} 48 | 49 | # 通信相关 50 | self.rsu_range:int =400 #RSU通信距离400m 51 | self.vehicle_range: int = 200 #车辆通信距离200m 52 | self.r2v_B:float=20 #R2V带宽：20Mbps 53 | self.v2v_B:float=40#V2V带宽:40Mbps 54 | self.rsu_p:float=50 #RSU发射功率：50w 55 | self.vehicle_p:float=10 #车发射功率： 10w 56 | self.w:float=0.001 #噪声功率𝜔：0.001 W/Hz 57 | self.k:float=30 #固定损耗𝐾：20-40db取30 58 | self.theta:int=2 #路径损耗因子𝜎：2-6取2 59 | self.r2r_onehop_time:float=8#r2r一跳传输时间8s 60 | self.c2r_rate:float=0.2#C-R传输速率：0.2mb/s 61 | # self.cloud_compute_ability:float=1800 #cloud计算能力15000Mcycles/s 62 | self.min_transfer_rate:float=0.01 #最小传输速率：0.01mb/s 63 | self.rsu_connect_time:float=10000 #RSU之间的联通时间 64 | self.cloud_connect_time:float=10000 #R2C的连通时间 65 | 66 | #惩罚 67 | self.punishment=-200 68 | 69 | #环境相关 70 | self.action_size=(self.rsu_number+self.vehicle_number+1)** 3#动作空间 71 | # 状态空间的最大值 72 | self.high = np.array([np.finfo(np.float32).max for _ in range(self.rsu_number+self.vehicle_number)]) 73 | # 状态空间的最小值 74 | self.low = np.array([0 for _ in range(self.rsu_number+self.vehicle_number)]) 75 | -------------------------------------------------------------------------------- /env/datastruct.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from typing import List # -> List[int] 指示应返回整数列表。 3 | 4 | 5 | #获取任务属性的类：获取任务信息大小，计算能力：每bit所需转数，任务延迟约束 6 | class Function(object): 7 | """任务属性及其操作""" 8 | #就是一个任务的三元数组 9 | 10 | def __init__( 11 | self, 12 | Function_task_datasize: float, 13 | Function_task_computing_resource: float, 14 | Function_task_delay: int 15 | 16 | ) -> None: 17 | self._Function_task_datasize = Function_task_datasize #感知任务的大小 18 | self._Function_task_computing_resource = Function_task_computing_resource #感知任务每bit的所需计算资源 19 | self._Function_task_delay = Function_task_delay #感知任务的延迟 20 | def get_task_datasize(self) -> float: 21 | return float(self._Function_task_datasize) 22 | 23 | def get_task_computing_resource(self) -> float: 24 | return float(self._Function_task_computing_resource) 25 | 26 | def get_task_delay(self) -> float: 27 | return float(self._Function_task_delay) 28 | 29 | 30 | 31 | ######################################################################## 32 | #操作队列的类：这个类的构造函数初始化了任务队列的属性，包括任务数量、任务大小范围、和随机数生成的种子 33 | #功能有：获取任务列表，返回节点的总任务量，任务列表增加任务，自己产生任务（随着时间变化），处理任务（随着时间变化） 34 | class TaskList(object): 35 | """节点上的待执行任务队列属性及操作""" 36 | 37 | def __init__( 38 | self, 39 | task_number: int, #节点上待执行任务的个数 40 | minimum_datasize: float, #节点上待执行任务大小的最小值 41 | maximum_datasize: float #节点上待执行任务大小的最小值 42 | # seed: int 43 | ) -> None: 44 | self._task_number = task_number 45 | self._minimum_datasize = minimum_datasize 46 | self._maximum_datasize = maximum_datasize 47 | # self._seed=seed 48 | 49 | 50 | # 生成每个任务的数据量大小 51 | # np.random.seed(self._seed) #设置种子确保每次使用相同的种子值运行代码时，都会得到相同的随机数序列。 52 | self._datasizes = np.random.uniform(self._minimum_datasize, self._maximum_datasize, self._task_number) 53 | #这一行生成一个数组（self._data_sizes），其中包含了在 _minimum_data_size 和 _maximum_data_size 之间均匀分布的随机浮点数。生成的随机值的数量等于 _task_number。 54 | self._task_list = [_ for _ in self._datasizes] #列表化 55 | 56 | def get_task_list(self) -> List[float]: 57 | return self._task_list 58 | 59 | def sum_datasize(self) -> float: 60 | """返回该节点的总任务量""" 61 | return sum(self._task_list) 62 | 63 | def add_task_list(self, new_data_size) -> None: 64 | """如果卸载到该节点，任务队列会增加""" 65 | self._task_list.append(new_data_size) 66 | 67 | def add_by_slot(self, task_number) -> None: 68 | """在时间转移中任务队列自动生成的任务""" 69 | data_sizes = np.random.uniform(self._minimum_datasize, self._maximum_datasize, task_number) 70 | for datasize in data_sizes: 71 | self._task_list.append(datasize) 72 | self._task_number += 1 73 | 74 | def delete_data_list(self, process_ability) -> None: 75 | """在时间转移中对任务队列中的任务进行处理""" 76 | while True: 77 | # 如果队列中没有任务 78 | if len(self._task_list) == 0: 79 | break 80 | # 如果队列中有任务 81 | elif process_ability >= self._task_list[0]: # 单位时间计算能力大于数据量 82 | process_ability -= self._task_list[0] 83 | del self._task_list[0] 84 | else: # 单位时间计算能力小于数据量 85 | self._task_list[0] -= process_ability 86 | break 87 | 88 | ######################################################################## 89 | #对每一辆车进行操作的类 90 | #功能有：获取生存时间，获取位置坐标，存货时间-1，判断是否不再存活，获取车辆行驶速度，获取车辆计算能力，获取车辆任务队列，获取车辆任务队列里的任务量之和 91 | 92 | class Vehicle(object): 93 | """车辆属性及其操作""" 94 | 95 | def __init__( 96 | self, 97 | road_range: int, #马路长度 98 | min_vehicle_speed: float, # 车辆最小行驶速度 99 | max_vehicle_speed: float, # 车辆最大行驶速度 100 | min_task_number: float, #车辆队列中任务最小个数 101 | max_task_number: float, #车辆队列中任务最大个数 102 | min_task_datasize: float, #每个任务大小的最大值 103 | max_task_datasize: float, # 每个任务大小的最小值 104 | min_vehicle_compute_ability: float, # 最小车辆计算能力 105 | max_vehicle_compute_ability: float, #最大车辆计算能力 106 | vehicle_x_initial_location: list, # 初始x坐标 107 | min_vehicle_y_initial_location: float, #初始y坐标最小值 108 | max_vehicle_y_initial_location: float,# 初始y坐标最大值 109 | seed: int 110 | ) -> None: 111 | # 车辆在场景中的生存时间生成 112 | self._road_range = road_range 113 | self._seed=seed 114 | #生成初始y坐标 115 | self._min_vehicle_y_initial_location=min_vehicle_y_initial_location 116 | self._max_vehicle_y_initial_location = max_vehicle_y_initial_location 117 | np.random.seed(self._seed) 118 | self._vehicle_y_initial_location = np.random.randint(self._min_vehicle_y_initial_location, self._max_vehicle_y_initial_location, 1)[0] 119 | # y坐标 120 | self._vehicle_y_location=self._vehicle_y_initial_location 121 | # 生成初始x坐标 122 | np.random.seed(self._seed) 123 | self._vehicle_x_initial_location=np.random.choice(vehicle_x_initial_location) 124 | # x坐标 125 | self._vehicle_x_location = self._vehicle_x_initial_location 126 | #生成速度 127 | np.random.seed(self._seed) 128 | self._vehicle_speed = np.random.randint(min_vehicle_speed, max_vehicle_speed) # 车辆速度 129 | if self._vehicle_x_initial_location==0: 130 | self._vehicle_speed = self._vehicle_speed 131 | else: 132 | self._vehicle_speed = -self._vehicle_speed 133 | # 生成存活时间 134 | self._stay_time = int(self._road_range / self._vehicle_speed) 135 | 136 | # 车辆计算能力生成 137 | self._max_compute_ability = max_vehicle_compute_ability 138 | self._min_compute_ability = min_vehicle_compute_ability 139 | 140 | # np.random.seed(self._seed) 141 | self._min_compute_ability = np.random.uniform(self._min_compute_ability, self._max_compute_ability, 1) 142 | 143 | # 车辆任务队列生成 144 | self._min_task_number = min_task_number 145 | self._max_task_number = max_task_number 146 | self._max_datasize = max_task_datasize 147 | self._min_datasize = min_task_datasize 148 | # np.random.seed(self._seed) 149 | self._task_number = np.random.randint(self._min_task_number, self._max_task_number) 150 | self._vehicle_task_list = TaskList(self._task_number, self._min_datasize, self._max_datasize) 151 | # self._vehicle_task_list = TaskList(self._task_number, self._min_datasize, self._max_datasize, self._seed) 152 | 153 | 154 | 155 | #获取原始信息 156 | def get_initial_data(self) -> list: 157 | 158 | data = [self._vehicle_x_initial_location, self._vehicle_y_initial_location,self._vehicle_speed] 159 | return data 160 | 161 | # 生存时间相关 162 | #获取车辆生存时间 163 | def get_stay_time(self) -> int: 164 | return self._stay_time 165 | 166 | #获取当前坐标变化 167 | def get_location(self) -> list: 168 | location = [self._vehicle_x_location, self._vehicle_y_location] 169 | return location 170 | 171 | 172 | #每秒坐标变化 173 | def change_location(self) -> list: 174 | self._vehicle_x_location = self._vehicle_x_location + self._vehicle_speed*1 175 | self._vehicle_y_location =self._vehicle_y_initial_location 176 | location = [self._vehicle_x_location, self._vehicle_y_location] 177 | return location 178 | 179 | 180 | #车辆生存时间-1 181 | def decrease_stay_time(self) -> int: 182 | self._stay_time -= 1 183 | return self._stay_time 184 | 185 | def is_out(self) -> bool: 186 | if self._stay_time <= 5: # 快要出去的车辆或者速度很大的车辆对任务不会感兴趣 187 | return True 188 | else: 189 | return False 190 | 191 | # 获取车辆行驶速度 192 | def get_vehicle_speed(self) -> float: 193 | return self._vehicle_speed 194 | 195 | #获取车辆计算速度 196 | def get_vehicle_compute_ability(self) -> float: 197 | return self._max_compute_ability 198 | 199 | # 车辆任务队列相关 200 | #获取车辆上的任务队列 201 | def get_task_list(self) -> TaskList: 202 | return self._vehicle_task_list 203 | 204 | # 获取车辆上的任务队列里所有任务的数据量之和 205 | def get_sum_tasks(self) -> float: 206 | if len(self._vehicle_task_list.get_task_list()) == 0: # 车辆上没有任务 207 | return 0 208 | else: 209 | return self._vehicle_task_list.sum_datasize() # 车辆上有任务 210 | 211 | 212 | 213 | ######################################################################## 214 | #对所有车辆进行操作的类 215 | # 功能有：获取车辆数量，获取车辆基础信息列表，增加车辆数量，从车辆队列中删除不在范围内的车辆 216 | class VehicleList(object): 217 | """实现场景中车辆的管理，包括车辆更新、停留时间更新以及任务队列更新""" 218 | 219 | def __init__( 220 | self, 221 | vehicle_number: int, #车辆个数 222 | road_range: int, #马路长度 223 | min_vehicle_speed: float, #车辆最小行驶速度 224 | max_vehicle_speed: float, #车辆最大行驶速度 225 | min_task_number: float, #车辆队列中任务最小个数 226 | max_task_number: float, #车辆队列中任务最大个数 227 | min_task_datasize: float, #车辆队列中任务大小的最小值 228 | max_task_datasize: float, #车辆队列中任务大小的最大值 229 | min_vehicle_compute_ability: float, #车辆计算速度的最小值 230 | max_vehicle_compute_ability: float, #车辆计算速度的最大值 231 | vehicle_x_initial_location: list, # 初始x坐标 232 | min_vehicle_y_initial_location: float, # 初始y坐标最小值 233 | max_vehicle_y_initial_location: float, # 初始y坐标最大值 234 | seed: int 235 | 236 | 237 | ) -> None: 238 | self._seed = seed 239 | self._vehicle_number = vehicle_number 240 | self._road_range = road_range 241 | self._min_vehicle_speed = min_vehicle_speed 242 | self._max_vehicle_speed = max_vehicle_speed 243 | self._min_task_number = min_task_number 244 | self._max_task_number = max_task_number 245 | self._min_datasize = min_task_datasize 246 | self._max_datasize = max_task_datasize 247 | self._min_compute_ability = min_vehicle_compute_ability 248 | self._max_compute_ability = max_vehicle_compute_ability 249 | self._vehicle_x_initial_location=vehicle_x_initial_location 250 | self._min_vehicle_y_initial_location= min_vehicle_y_initial_location 251 | self._max_vehicle_y_initial_location= max_vehicle_y_initial_location 252 | 253 | 254 | #车辆基础信息列表，n辆车就有n个信息组 255 | self.vehicle_list = [ 256 | Vehicle( 257 | road_range=self._road_range, 258 | min_vehicle_speed=self._min_vehicle_speed, 259 | max_vehicle_speed=self._max_vehicle_speed, 260 | min_task_number=self._min_task_number, 261 | max_task_number=self._max_task_number, 262 | min_task_datasize=self._min_datasize, 263 | max_task_datasize=self._max_datasize, 264 | min_vehicle_compute_ability=self._min_compute_ability, 265 | max_vehicle_compute_ability=self._max_compute_ability, 266 | vehicle_x_initial_location=self._vehicle_x_initial_location, # 初始x坐标 267 | min_vehicle_y_initial_location=self._min_vehicle_y_initial_location, # 初始y坐标最小值 268 | max_vehicle_y_initial_location=self._max_vehicle_y_initial_location, # 初始y坐标最大值 269 | seed=self._seed+_ 270 | ) 271 | for _ in range(self._vehicle_number)] 272 | 273 | def get_vehicle_number(self) -> int: 274 | """获取车辆数量""" 275 | return self._vehicle_number 276 | 277 | def get_vehicle_list(self) -> List[Vehicle]: 278 | """获取车辆基础信息队列""" 279 | return self.vehicle_list 280 | 281 | def add_stay_vehicle(self, new_vehicle_number,time_now) -> None: 282 | """增加车辆数量""" 283 | # np.random.seed(self._seed) 284 | new_vehicle_list = [ 285 | Vehicle( 286 | road_range=self._road_range, 287 | min_vehicle_speed=self._min_vehicle_speed, 288 | max_vehicle_speed=self._max_vehicle_speed, 289 | min_task_number=self._min_task_number, 290 | max_task_number=self._max_task_number, 291 | min_task_datasize=self._min_datasize, 292 | max_task_datasize=self._max_datasize, 293 | min_vehicle_compute_ability=self._min_compute_ability, 294 | max_vehicle_compute_ability=self._max_compute_ability, 295 | vehicle_x_initial_location=self._vehicle_x_initial_location, # 初始x坐标 296 | min_vehicle_y_initial_location=self._min_vehicle_y_initial_location, # 初始y坐标最小值 297 | max_vehicle_y_initial_location=self._max_vehicle_y_initial_location, # 初始y坐标最大值 298 | seed=time_now+_ 299 | ) 300 | for _ in range(new_vehicle_number)] 301 | 302 | self.vehicle_list = self.vehicle_list + new_vehicle_list 303 | self._vehicle_number += new_vehicle_number 304 | 305 | def delete_out_vehicle(self) -> None: 306 | """从队列中删除不在范围内的车辆""" 307 | i = 0 308 | while i < len(self.vehicle_list): 309 | if len(self.vehicle_list) == 0:#不一定需要判断 310 | pass 311 | elif self.vehicle_list[i].is_out(): 312 | del self.vehicle_list[i] 313 | self._vehicle_number -= 1 314 | else: 315 | i += 1 316 | ######################################################################## 317 | #对某个RSU进行操作的类 318 | #功能有：获取RSU计算能力，获取RSU任务队列，获取RSU任务队列上的任务量之和 319 | class RSU(object): 320 | """RSU""" 321 | 322 | def __init__( 323 | self, 324 | min_task_number: float, #RSU队列中任务最小个数 325 | max_task_number: float, #RSU队列中任务最大个数 326 | min_task_datasize: float, # RSU队列中任务大小的最小值 327 | max_task_datasize: float, #RSU队列中任务大小的最大值 328 | min_rsu_compute_ability: float, # RSU计算速度的最小值 329 | max_rsu_compute_ability: float #RSU计算速度的最大值 330 | # seed: int 331 | ) -> None: 332 | # rsu计算速度生成 333 | self._max_compute_ability = max_rsu_compute_ability 334 | self._min_compute_ability = min_rsu_compute_ability 335 | # self._seed = seed 336 | # np.random.seed(self._seed) 337 | self._compute_ability = np.random.uniform(self._min_compute_ability, self._max_compute_ability, 1) 338 | 339 | # 车辆任务队列生成 340 | self._min_task_number = min_task_number 341 | self._max_task_number = max_task_number 342 | self._max_datasize = max_task_datasize 343 | self._min_datasize = min_task_datasize 344 | # np.random.seed(self._seed) 345 | self._task_number = np.random.randint(self._min_task_number, self._max_task_number) 346 | self._rsu_task_list = TaskList(self._task_number, self._min_datasize, self._max_datasize) 347 | # self._rsu_task_list = TaskList(self._task_number, self._min_datasize, self._max_datasize, self._seed) 348 | 349 | #获取RSU计算速度 350 | def get_rsu_compute_ability(self) -> float: 351 | return self._compute_ability 352 | 353 | # 获取RSU任务队列 354 | def get_task_list(self) -> TaskList: 355 | return self._rsu_task_list 356 | 357 | # 获取RSU上的任务队列里所有任务的数据量之和 358 | def get_sum_tasks(self) -> float: 359 | if len(self._rsu_task_list.get_task_list()) == 0: # RSU上没有任务 360 | return 0 361 | else: 362 | return self._rsu_task_list.sum_datasize() # RSU上有任务 363 | 364 | 365 | 366 | ######################################################################## 367 | #对所有RSU进行操作的类 368 | #获取RSU个数，获取RSU上的基础信息组 369 | class RSUList(object): 370 | """RSU队列管理""" 371 | 372 | def __init__( 373 | self, 374 | rsu_number, #RSU个数 375 | min_task_number: float, # RSU队列中任务最小个数 376 | max_task_number: float, #RSU队列中任务最大个数 377 | min_task_datasize: float, # RSU队列中任务大小的最小值 378 | max_task_datasize: float, #RSU队列中任务大小的最大值 379 | min_rsu_compute_ability: float, # RSU计算速度的最小值 380 | max_rsu_compute_ability: float #RSU计算速度的最大值 381 | # seed: int 382 | ) -> None: 383 | # self._seed = seed 384 | self._rsu_number = rsu_number 385 | 386 | self._min_task_number = min_task_number 387 | self._max_task_number = max_task_number 388 | self._min_datasize = min_task_datasize 389 | self._max_datasize = max_task_datasize 390 | self._min_compute_ability = min_rsu_compute_ability 391 | self._max_compute_ability = max_rsu_compute_ability 392 | 393 | # 获取RSU类 394 | self.rsu_list = [ 395 | RSU( 396 | min_task_number=self._min_task_number, 397 | max_task_number=self._max_task_number, 398 | min_task_datasize=self._min_datasize, 399 | max_task_datasize=self._max_datasize, 400 | min_rsu_compute_ability=self._min_compute_ability, 401 | max_rsu_compute_ability=self._max_compute_ability 402 | # seed=self._seed+_ 403 | ) 404 | for _ in range(rsu_number)] 405 | 406 | #获取RSU个数 407 | def get_rsu_number(self): 408 | return self._rsu_number 409 | #获取RSU的基础信息组 410 | def get_rsu_list(self): 411 | return self.rsu_list 412 | 413 | #对时隙进行操作的类 414 | class TimeSlot(object): 415 | """时隙属性及操作""" 416 | 417 | def __init__(self, start: int, end: int) -> None: 418 | self.start = start #时间起始间隙 419 | self.end = end #时间截止间隙 420 | self.slot_length = self.end - self.start #时间间隙长度 421 | 422 | self.now = start #当前时间间隙定位 423 | self.reset() #做一些操作来将对象的属性或状态还原到初始状态 424 | 425 | def __str__(self): 426 | return f"now time: {self.now}, [{self.start} , {self.end}] with {self.slot_length} slots" 427 | 428 | #随着时间增加 429 | def add_time(self) -> None: 430 | """add time to the system""" 431 | self.now += 1 432 | 433 | #当前是否在时间截止间隙 434 | def is_end(self) -> bool: 435 | """check if the system is at the end of the time slots""" 436 | return self.now >= self.end 437 | #获取时间间隙长度 438 | def get_slot_length(self) -> int: 439 | """get the length of each time slot""" 440 | return self.slot_length 441 | #获取当前时间间隙定位 442 | def get_now(self) -> int: 443 | return self.now 444 | #重置 445 | def reset(self) -> None: 446 | self.now = self.start 447 | -------------------------------------------------------------------------------- /env/utils.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding=utf-8 3 | """ 4 | Author: John 5 | Email: johnjim0816@gmail.com 6 | Date: 2021-03-12 16:02:24 7 | LastEditor: John 8 | LastEditTime: 2022-07-13 22:15:46 9 | Description: 10 | Environment: 11 | """ 12 | import os 13 | import numpy as np 14 | from pathlib import Path 15 | import matplotlib.pyplot as plt 16 | import seaborn as sns 17 | 18 | from matplotlib.font_manager import FontProperties # 导入字体模块 19 | 20 | 21 | def chinese_font(): 22 | """ 设置中文字体，注意需要根据自己电脑情况更改字体路径，否则还是默认的字体 """ 23 | try: 24 | font = FontProperties( 25 | fname='C:/Windows/Fonts/STSONG.TTF', size=15) # fname系统字体路径，此处是windows的 26 | except: 27 | font = None 28 | return font 29 | 30 | 31 | 32 | 33 | 34 | 35 | def plot_rewards_cn(rewards, ma_rewards, cfg, tag='train'): 36 | """ 中文画图 """ 37 | sns.set() 38 | plt.figure() 39 | plt.title(u"{}环境下{}算法的学习曲线".format(cfg.env_name, cfg.algo_name), fontproperties=chinese_font()) 40 | plt.xlabel(u'回合数', fontproperties=chinese_font()) 41 | plt.plot(rewards) 42 | plt.plot(ma_rewards) 43 | plt.legend((u'奖励', u'滑动平均奖励',), loc="best", prop=chinese_font()) 44 | if cfg.save: 45 | plt.savefig(cfg.result_path + f"{tag}_rewards_curve_cn.eps", format='eps', dpi=1000) 46 | plt.show() 47 | 48 | 49 | def plot_rewards(rewards, ma_rewards, cfg, tag='train'): 50 | sns.set() 51 | plt.figure() # 创建一个图形实例，方便同时多画几个图 52 | plt.title("learning curve on {} of {}".format(cfg.device, cfg.algo_name), fontsize=18) 53 | plt.xlabel('epsiodes', fontsize=18) 54 | plt.plot(rewards, label='rewards') 55 | plt.plot(ma_rewards, label='ma rewards') 56 | plt.legend() 57 | plt.grid() 58 | if cfg.save_fig: 59 | plt.savefig(cfg.result_path + "{}_rewards_curve.eps".format(tag), format='eps', dpi=1000) 60 | plt.show() 61 | 62 | 63 | def plot_completion_rate(completion_rate, ma_completion_rate, cfg, tag='train'): 64 | # sns.set() 65 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 66 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 67 | plt.figure() # 创建一个图形实例，方便同时多画几个图 68 | # plt.title("learning curve on {} of {}".format(cfg.device, cfg.algo_name), fontsize=18) 69 | plt.xticks( fontsize=16, fontname='Times New Roman') 70 | plt.yticks( fontsize=16, fontname='Times New Roman') 71 | plt.xlabel('episodes', fontsize=18, fontname='Times New Roman') 72 | plt.ylabel('completion ratio', fontsize=18, fontname='Times New Roman') 73 | plt.plot(completion_rate, label='completion_rate') 74 | plt.plot(ma_completion_rate, label='ma_completion_rate') 75 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 76 | plt.legend(prop={'size':18, 'family': 'Times New Roman'}) 77 | if cfg.save_fig: 78 | plt.savefig(cfg.result_path + "{}_completion_rate_curve.eps".format(tag), format='eps', dpi=1000) 79 | plt.show() 80 | ######################################################################################################################## 81 | def plot_A3C_rewards(A3C_train_ma_rewards_1,A3C_train_ma_rewards_2,A3C_train_ma_rewards_3): 82 | # sns.set() 83 | plt.figure() # 创建一个图形实例，方便同时多画几个图 84 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 85 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 86 | # plt.title("Convergence graph for different number of processess", fontsize=14) 87 | plt.xlabel('episodes', fontsize=26, fontname='Times New Roman') 88 | plt.ylabel('rewards', fontsize=26, fontname='Times New Roman') 89 | plt.xticks( fontsize=22, fontname='Times New Roman') 90 | plt.yticks( fontsize=22, fontname='Times New Roman') 91 | plt.plot(A3C_train_ma_rewards_1, label='number of training processes=2') 92 | plt.plot(A3C_train_ma_rewards_2, label='number of training processes=4') 93 | plt.plot(A3C_train_ma_rewards_3, label='number of training processes=6') 94 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 95 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 96 | plt.tight_layout() 97 | plt.savefig('A3C_rewards.pdf', format='pdf') 98 | plt.show() 99 | 100 | 101 | def plot_A3C_rewards_lr(A3C_train_ma_rewards_1_lr,A3C_train_ma_rewards_2_lr,A3C_train_ma_rewards_3_lr): 102 | # sns.set() 103 | plt.figure() # 创建一个图形实例，方便同时多画几个图 104 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 105 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 106 | # plt.title("Convergence graph for different number of processess", fontsize=14) 107 | plt.xlabel('episodes', fontsize=26, fontname='Times New Roman') 108 | plt.ylabel('rewards', fontsize=26, fontname='Times New Roman') 109 | plt.xticks( fontsize=22, fontname='Times New Roman') 110 | plt.yticks( fontsize=22, fontname='Times New Roman') 111 | plt.plot(A3C_train_ma_rewards_1_lr, label='lr=0.002') 112 | plt.plot(A3C_train_ma_rewards_2_lr, label='lr=0.0002') 113 | plt.plot(A3C_train_ma_rewards_3_lr, label='lr=0.00002') 114 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 115 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 116 | plt.tight_layout() 117 | plt.savefig('A3C_rewards_lr.pdf', format='pdf') 118 | plt.show() 119 | 120 | 121 | def plot_contrast_rewards(A3C_train_ma_rewards,Greedy_train_ma_rewards,DQN_train_ma_rewards): 122 | # sns.set() 123 | plt.figure() # 创建一个图形实例，方便同时多画几个图 124 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 125 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 126 | # plt.title("Convergence graphs of different algorithms", fontsize=14) 127 | plt.xticks( fontsize=22, fontname='Times New Roman') 128 | plt.yticks( fontsize=22, fontname='Times New Roman') 129 | 130 | plt.xlabel('episodes', fontsize=26, fontname='Times New Roman') 131 | plt.ylabel('rewards', fontsize=26, fontname='Times New Roman') 132 | plt.plot(A3C_train_ma_rewards, label='A3C') 133 | plt.plot(Greedy_train_ma_rewards, label='Greedy') 134 | plt.plot(DQN_train_ma_rewards, label='DQN') 135 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 136 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 137 | plt.tight_layout() 138 | plt.savefig('contrast_rewards.pdf', format='pdf') 139 | plt.show() 140 | 141 | 142 | def plot_A3C_completion_rate(A3C_train_ma_completion_rate_1,A3C_train_ma_completion_rate_2,A3C_train_ma_completion_rate_3): 143 | # sns.set() 144 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 145 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 146 | plt.figure() # 创建一个图形实例，方便同时多画几个图 147 | # plt.title("Convergence graph for different number of processess", fontsize=14) 148 | plt.xticks( fontsize=22, fontname='Times New Roman') 149 | plt.yticks( fontsize=22, fontname='Times New Roman') 150 | plt.xlabel('episodes', fontsize=26, fontname='Times New Roman') 151 | plt.ylabel('completion ratio', fontsize=26, fontname='Times New Roman') 152 | plt.plot(A3C_train_ma_completion_rate_1, label='number of training processes=2') 153 | plt.plot(A3C_train_ma_completion_rate_2, label='number of training processes=4') 154 | plt.plot(A3C_train_ma_completion_rate_3, label='number of training processes=6') 155 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 156 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 157 | plt.tight_layout() 158 | plt.savefig('A3C_completion_rate.pdf', format='pdf') 159 | plt.show() 160 | 161 | 162 | 163 | def plot_A3C_completion_rate_lr(A3C_train_ma_completion_rate_1_lr,A3C_train_ma_completion_rate_2_lr,A3C_train_ma_completion_rate_3_lr): 164 | # sns.set() 165 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 166 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 167 | plt.figure() # 创建一个图形实例，方便同时多画几个图 168 | # plt.title("Convergence graph for different number of processess", fontsize=14) 169 | plt.xticks( fontsize=22, fontname='Times New Roman') 170 | plt.yticks( fontsize=22, fontname='Times New Roman') 171 | plt.xlabel('episodes', fontsize=26, fontname='Times New Roman') 172 | plt.ylabel('completion ratio', fontsize=26, fontname='Times New Roman') 173 | plt.plot(A3C_train_ma_completion_rate_1_lr, label='lr=0.002') 174 | plt.plot(A3C_train_ma_completion_rate_2_lr, label='lr=0.0002') 175 | plt.plot(A3C_train_ma_completion_rate_3_lr, label='lr=0.00002') 176 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 177 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 178 | plt.tight_layout() 179 | plt.savefig('A3C_completion_rate_lr.pdf', format='pdf') 180 | plt.show() 181 | 182 | 183 | def plot_contrast_completion_rate(A3C_train_ma_completion_rate,Greedy_train_ma_completion_rate,DQN_train_ma_completion_rate): 184 | # sns.set() 185 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 186 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 187 | plt.figure() # 创建一个图形实例，方便同时多画几个图 188 | # plt.title("Convergence graphs of different algorithms", fontsize=14) 189 | plt.xticks( fontsize=22, fontname='Times New Roman') 190 | plt.yticks( fontsize=22, fontname='Times New Roman') 191 | plt.xlabel('episodes', fontsize=26, fontname='Times New Roman') 192 | plt.ylabel('completion ratio', fontsize=26, fontname='Times New Roman') 193 | plt.plot(A3C_train_ma_completion_rate, label='A3C') 194 | plt.plot(Greedy_train_ma_completion_rate, label='Greedy') 195 | plt.plot(DQN_train_ma_completion_rate, label='DQN') 196 | plt.grid(True, linestyle='--', linewidth=0.5, color='gray') 197 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 198 | plt.tight_layout() 199 | plt.savefig('contrast_completion_rate.pdf', format='pdf') 200 | plt.show() 201 | 202 | 203 | def plot_different_tasksize_average_rewards(A3C_average,Greedy_average,DQN_average): 204 | # sns.set() 205 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 206 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 207 | plt.figure() # 创建一个图形实例，方便同时多画几个图 208 | # plt.title("different tasksize average rewards", fontsize=18) 209 | plt.xlabel('size of tasks', fontsize=26, fontname='Times New Roman') 210 | plt.ylabel('average rewards', fontsize=26, fontname='Times New Roman') 211 | x_datasize=[2,3,4,5,6] 212 | A3C_y_datasize=A3C_average 213 | Greedy_y_datasize= Greedy_average 214 | DQN_y_datasize=DQN_average 215 | plt.plot(x_datasize, A3C_y_datasize,label='A3C', linestyle='-', marker='o', markersize=8, markerfacecolor='red') 216 | plt.plot(x_datasize, Greedy_y_datasize,label='Greedy', linestyle='-', marker='s', markersize=8, markerfacecolor='orange') 217 | plt.plot(x_datasize, DQN_y_datasize,label='DQN', linestyle='-', marker='D', markersize=8, markerfacecolor='blue') 218 | plt.xticks(x_datasize,fontsize=22, fontname='Times New Roman') 219 | plt.yticks( fontsize=22, fontname='Times New Roman') 220 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 221 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 222 | plt.tight_layout() 223 | plt.savefig('different_tasksize_average_rewards.pdf', format='pdf') 224 | plt.show() 225 | 226 | 227 | 228 | 229 | def plot_different_vehicle_number_average_rewards(A3C_average_vehicle_number,Greedy_average_vehicle_number,DQN_average_vehicle_number): 230 | # sns.set() 231 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 232 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 233 | plt.figure() # 创建一个图形实例，方便同时多画几个图 234 | # plt.title("different vehicle number average rewards", fontsize=18) 235 | plt.xlabel('number of vehicles', fontsize=26, fontname='Times New Roman') 236 | plt.ylabel('average rewards', fontsize=26, fontname='Times New Roman') 237 | x_datasize = [8, 9, 10] 238 | A3C_y_datasize = A3C_average_vehicle_number 239 | Greedy_y_datasize = Greedy_average_vehicle_number 240 | DQN_y_datasize = DQN_average_vehicle_number 241 | plt.plot(x_datasize, A3C_y_datasize, label='A3C', linestyle='-', marker='o', markersize=8, markerfacecolor='red') 242 | plt.plot(x_datasize, Greedy_y_datasize, label='Greedy', linestyle='-', marker='s', markersize=8, 243 | markerfacecolor='orange') 244 | plt.plot(x_datasize, DQN_y_datasize, label='DQN', linestyle='-', marker='D', markersize=8, markerfacecolor='blue') 245 | plt.xticks(x_datasize,fontsize=22, fontname='Times New Roman') 246 | plt.yticks( fontsize=22, fontname='Times New Roman') 247 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 248 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 249 | plt.tight_layout() 250 | plt.savefig('different_vehicle_number_average_rewards.pdf', format='pdf') 251 | plt.show() 252 | 253 | 254 | 255 | 256 | def plot_different_vehicle_speed_average_rewards(A3C_speed,Greedy_speed,DQN_speed): 257 | # sns.set() 258 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 259 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 260 | plt.figure() # 创建一个图形实例，方便同时多画几个图 261 | # plt.title("different vehicle speed average rewards", fontsize=18) 262 | plt.xlabel('speed of vehicles', fontsize=26, fontname='Times New Roman') 263 | plt.ylabel('average rewards', fontsize=26, fontname='Times New Roman') 264 | x_datasize=['20-25','25-30','30-35'] 265 | A3C_y_speed=A3C_speed 266 | Greedy_y_speed= Greedy_speed 267 | DQN_y_speed=DQN_speed 268 | plt.xticks(fontsize=22, fontname='Times New Roman') 269 | plt.yticks( fontsize=22, fontname='Times New Roman') 270 | plt.plot(x_datasize, A3C_y_speed,label='A3C', linestyle='-', marker='o', markersize=8, markerfacecolor='red') 271 | plt.plot(x_datasize, Greedy_y_speed,label='Greedy', linestyle='-', marker='s', markersize=8, markerfacecolor='orange') 272 | plt.plot(x_datasize, DQN_y_speed,label='DQN', linestyle='-', marker='D', markersize=8, markerfacecolor='blue') 273 | plt.xticks(x_datasize,fontsize=22, fontname='Times New Roman') 274 | plt.yticks( fontsize=22, fontname='Times New Roman') 275 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 276 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 277 | plt.tight_layout() 278 | plt.savefig('different_vehicle_speed_average_rewards.pdf', format='pdf') 279 | plt.show() 280 | 281 | 282 | def plot_different_task_computation_resource_average_rewards(A3C_task_computation_resource,Greedy_task_computation_resource,DQN_task_computation_resource): 283 | # sns.set() 284 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 285 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 286 | plt.figure() # 创建一个图形实例，方便同时多画几个图 287 | # plt.title("different vehicle speed average rewards", fontsize=18) 288 | plt.xlabel('computation intensity of tasks', fontsize=26, fontname='Times New Roman') 289 | plt.ylabel('average rewards', fontsize=26, fontname='Times New Roman') 290 | x_datasize=['300','325','350','375','400'] 291 | A3C_y_task_computation_resource=A3C_task_computation_resource 292 | Greedy_y_task_computation_resource= Greedy_task_computation_resource 293 | DQN_y_task_computation_resource=DQN_task_computation_resource 294 | plt.xticks(fontsize=22, fontname='Times New Roman') 295 | plt.yticks( fontsize=22, fontname='Times New Roman') 296 | plt.plot(x_datasize, A3C_y_task_computation_resource,label='A3C', linestyle='-', marker='o', markersize=8, markerfacecolor='red') 297 | plt.plot(x_datasize, Greedy_y_task_computation_resource,label='Greedy', linestyle='-', marker='s', markersize=8, markerfacecolor='orange') 298 | plt.plot(x_datasize, DQN_y_task_computation_resource,label='DQN', linestyle='-', marker='D', markersize=8, markerfacecolor='blue') 299 | plt.xticks(x_datasize,fontsize=22, fontname='Times New Roman') 300 | plt.yticks( fontsize=22, fontname='Times New Roman') 301 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 302 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 303 | plt.tight_layout() 304 | plt.savefig('different_task_computation_resource_average_rewards.pdf', format='pdf') 305 | plt.show() 306 | 307 | 308 | ######################################################################################################################## 309 | 310 | def plot_different_vehicle_speed_average_rewards_1(A3C_average_vehicle_speed,DQN_average_vehicle_speed,Greedy_average_vehicle_speed): 311 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 312 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 313 | plt.figure() # 创建一个图形实例，方便同时多画几个图 314 | plt.xlabel('speed of vehicles', fontsize=26, fontname='Times New Roman') 315 | plt.ylabel('average rewards', fontsize=26, fontname='Times New Roman') 316 | Width = 0.2 317 | x1 = np.arange(len(A3C_average_vehicle_speed)) 318 | x2=[x + Width for x in x1] 319 | x3=[x + Width for x in x2] 320 | A3C_y=A3C_average_vehicle_speed 321 | Greedy_y = Greedy_average_vehicle_speed 322 | DQN_y = DQN_average_vehicle_speed 323 | plt.xticks(fontsize=22, fontname='Times New Roman') 324 | plt.yticks( fontsize=22, fontname='Times New Roman') 325 | plt.bar(x1, A3C_y,label='A3C',width=Width,color='red') 326 | plt.bar(x2, DQN_y, label='DQN',width=Width,color='orange') 327 | plt.bar(x3, Greedy_y, label='Greedy',width=Width,color='green') 328 | plt.xticks([r + Width for r in range(len(A3C_average_vehicle_speed))], ['20-25','25-30','30-35']) 329 | # 获取 y 轴刻度标签 330 | ticks = plt.gca().get_yticks() 331 | # 在每个刻度前面加上负号 332 | tick_labels = ['-' + str(int(abs(t))) if t != 0 else '0' for t in ticks] 333 | # 设置 y 轴刻度标签 334 | plt.yticks(ticks, tick_labels) 335 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 336 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 337 | plt.tight_layout() 338 | plt.savefig('different_vehicle_speed_average_rewards.pdf', format='pdf') 339 | plt.show() 340 | 341 | 342 | def plot_different_vehicle_number_average_rewards_1(A3C_average_rewards_vehicle_number,DQN_average_rewards_vehicle_number,Greedy_average_rewards_vehicle_number): 343 | plt.rcParams['figure.facecolor'] = 'white' # 设置画布背景颜色为白色 344 | plt.rcParams['axes.facecolor'] = 'white' # 设置坐标轴背景颜色为白色 345 | plt.figure() # 创建一个图形实例，方便同时多画几个图 346 | plt.xlabel('number of vehicles', fontsize=26, fontname='Times New Roman') 347 | plt.ylabel('average rewards', fontsize=26, fontname='Times New Roman') 348 | Width = 0.2 349 | x1 = np.arange(len(A3C_average_rewards_vehicle_number)) 350 | x2=[x + Width for x in x1] 351 | x3=[x + Width for x in x2] 352 | A3C_y=A3C_average_rewards_vehicle_number 353 | Greedy_y = Greedy_average_rewards_vehicle_number 354 | DQN_y = DQN_average_rewards_vehicle_number 355 | plt.xticks(fontsize=22, fontname='Times New Roman') 356 | plt.yticks( fontsize=22, fontname='Times New Roman') 357 | plt.bar(x1, A3C_y,label='A3C',width=Width,color='red') 358 | plt.bar(x2, DQN_y, label='DQN',width=Width,color='orange') 359 | plt.bar(x3, Greedy_y, label='Greedy',width=Width,color='green') 360 | plt.xticks([r + Width for r in range(len(A3C_average_rewards_vehicle_number))], ['8','9','10']) 361 | # 获取 y 轴刻度标签 362 | ticks = plt.gca().get_yticks() 363 | # 在每个刻度前面加上负号 364 | tick_labels = ['-' + str(int(abs(t))) if t != 0 else '0' for t in ticks] 365 | # 设置 y 轴刻度标签 366 | plt.yticks(ticks, tick_labels) 367 | plt.grid(True,linestyle='--', linewidth=0.5, color='gray') 368 | plt.legend(prop={'size': 18, 'family': 'Times New Roman'}) 369 | plt.tight_layout() 370 | plt.savefig('different_vehicle_number_average_rewards.pdf', format='pdf') 371 | plt.show() 372 | 373 | 374 | 375 | def plot_losses(losses, algo="DQN", save=True, path='./'): 376 | sns.set() 377 | plt.figure() 378 | plt.title("loss curve of {}".format(algo), fontsize=18) 379 | plt.xlabel('epsiodes', fontsize=18) 380 | plt.plot(losses, label='rewards') 381 | plt.legend() 382 | if save: 383 | plt.savefig(path + "losses_curve.eps", format='eps', dpi=1000) 384 | plt.show() 385 | 386 | 387 | def save_results_1(dic, tag='train', path='./results'): 388 | """ 保存奖励 """ 389 | for key, value in dic.items(): 390 | np.save(path + '{}_{}.npy'.format(tag, key), value) 391 | print('Results saved！') 392 | 393 | 394 | def save_results(rewards, ma_rewards, tag='train', path='./results'): 395 | """ 保存奖励 """ 396 | np.save(path + '{}_rewards.npy'.format(tag), rewards) 397 | np.save(path + '{}_ma_rewards.npy'.format(tag), ma_rewards) 398 | print('Result saved!') 399 | 400 | 401 | 402 | def make_dir(*paths): 403 | """ 创建文件夹 """ 404 | for path in paths: 405 | Path(path).mkdir(parents=True, exist_ok=True) 406 | 407 | 408 | def del_empty_dir(*paths): 409 | """ 删除目录下所有空文件夹 """ 410 | for path in paths: 411 | dirs = os.listdir(path) 412 | for dir in dirs: 413 | if not os.listdir(os.path.join(path, dir)): 414 | os.removedirs(os.path.join(path, dir)) 415 | 416 | 417 | def save_args(args): 418 | # save parameters 419 | argsDict = args.__dict__ 420 | with open(args.result_path + 'params.txt', 'w') as f: 421 | f.writelines('------------------ start ------------------' + '\n') 422 | for eachArg, value in argsDict.items(): 423 | f.writelines(eachArg + ' : ' + str(value) + '\n') 424 | f.writelines('------------------- end -------------------') 425 | print("Parameters saved!") 426 | 427 | 428 | 429 | # 430 | # def save_results_from_npy_to_txt(file_path, output_path): 431 | # """ 从.npy文件中提取数据并保存为文本文件 """ 432 | # data = np.load(file_path) 433 | # with open(output_path, 'w') as file: 434 | # for item in data: 435 | # file.write(' '.join(str(x) for x in item) + '\n') 436 | # print('Results extracted from .npy file and saved to text file！') 437 | 438 | 439 | 440 | # def plot_offloading_rate(offloading_rate_c, offloading_rate_r, offloading_rate_v, cfg, tag='train'): 441 | # sns.set() 442 | # plt.figure() # 创建一个图形实例，方便同时多画几个图 443 | # plt.title("offloading rate curve on {} of {}".format(cfg.device, cfg.algo_name), fontsize=18) 444 | # plt.xlabel('epsiodes', fontsize=18) 445 | # plt.plot(offloading_rate_c, label='offloading_rate_c') 446 | # plt.plot(offloading_rate_r, label='offloading_rate_r') 447 | # plt.plot(offloading_rate_v, label='offloading_rate_v') 448 | # plt.legend() 449 | # if cfg.save_fig: 450 | # plt.savefig(cfg.result_path + "{}_offloading_rate_curve.eps".format(tag), format='eps', dpi=1000) 451 | # plt.show() 452 | 453 | 454 | 455 | # def plot_finish_rate(finish_rate, ma_finish_rate, cfg, tag='train'): 456 | # sns.set() 457 | # plt.figure() # 创建一个图形实例，方便同时多画几个图 458 | # plt.title("finish rate curve on {} of {}".format(cfg.device, cfg.algo_name), fontsize=18) 459 | # plt.xlabel('epsiodes', fontsize=18) 460 | # plt.plot(finish_rate, label='finish rate') 461 | # plt.plot(ma_finish_rate, label='ma finish rate') 462 | # plt.legend() 463 | # if cfg.save_fig: 464 | # plt.savefig(cfg.result_path + "{}_finish_rate_curve.eps".format(tag), format='eps', dpi=1000) 465 | # plt.show() 466 | 467 | 468 | 469 | -------------------------------------------------------------------------------- /methods/A3C/a3c.py: -------------------------------------------------------------------------------- 1 | import torch.optim as optim 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from torch.distributions import Categorical 5 | import torch 6 | import os 7 | class ActorCritic(nn.Module): 8 | def __init__(self,input_dim, output_dim, hidden_dim): 9 | super(ActorCritic, self).__init__() 10 | self.fc1 = nn.Linear(input_dim, hidden_dim) 11 | self.fc_actor = nn.Linear( hidden_dim, output_dim) 12 | self.fc_critic = nn.Linear( hidden_dim, 1) 13 | 14 | def actor(self, x, softmax_dim=0): 15 | x = F.relu(self.fc1(x)) 16 | x = self.fc_actor(x) 17 | prob = F.softmax(x, dim=softmax_dim) 18 | return prob 19 | 20 | def critic(self, x): 21 | x = F.relu(self.fc1(x)) 22 | v = self.fc_critic(x) 23 | return v 24 | 25 | 26 | 27 | 28 | def save(self, path): 29 | checkpoint = os.path.join(path, 'a3c.pt') 30 | torch.save(self.state_dict(), checkpoint) 31 | 32 | 33 | def load(self, path): 34 | checkpoint = os.path.join(path, 'a3c.pt') 35 | self.aload_state_dict(torch.load(checkpoint)) 36 | 37 | 38 | # import torch.optim as optim 39 | # import torch.nn as nn 40 | # import torch.nn.functional as F 41 | # from torch.distributions import Categorical 42 | # import torch 43 | # import os 44 | # 45 | # class ActorCritic(nn.Module): 46 | # def __init__(self, input_dim, output_dim, hidden_dim, device): 47 | # super(ActorCritic, self).__init__() 48 | # self.fc1 = nn.Linear(input_dim, hidden_dim).to(device) 49 | # self.fc_actor = nn.Linear(hidden_dim, output_dim).to(device) 50 | # self.fc_critic = nn.Linear(hidden_dim, 1).to(device) 51 | # 52 | # def actor(self, x, softmax_dim=0): 53 | # x = F.relu(self.fc1(x)) 54 | # x = self.fc_actor(x) 55 | # prob = F.softmax(x, dim=softmax_dim) 56 | # return prob 57 | # 58 | # def critic(self, x): 59 | # x = F.relu(self.fc1(x)) 60 | # v = self.fc_critic(x) 61 | # return v 62 | # 63 | # def save(self, path): 64 | # checkpoint = os.path.join(path, 'a3c.pt') 65 | # state_dict_on_cpu = {key: val.cpu() for key, val in self.state_dict().items()} 66 | # torch.save(state_dict_on_cpu, checkpoint) 67 | # 68 | # def load(self, path, device): 69 | # checkpoint = os.path.join(path, 'a3c.pt') 70 | # state_dict = torch.load(checkpoint, map_location=device) 71 | # self.load_state_dict(state_dict) 72 | -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20230926-211815/models/a3c.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZWLab23/Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV-Networks/8a3529dc3eeb6f7afd367288134456ac2f9740e6/methods/A3C/outputs/Multihop-V2V/20230926-211815/models/a3c.pt -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20230926-211815/results/config_params.txt: -------------------------------------------------------------------------------- 1 | import dataclasses 2 | # 旨在简化数据类的定义，减少样板代码，并提供更好的代码可读性。这对于处理大量数据对象的情况特别有用。 3 | import numpy as np 4 | 5 | 6 | @dataclasses.dataclass 7 | class VehicularEnvConfig: 8 | def __init__(self): 9 | # 道路信息 10 | self.road_range: int = 1200 # 500m 11 | self.road_width: int = 50 # 500m 12 | 13 | # 时间信息 14 | self.time_slot_start: int = 0 15 | self.time_slot_end: int = 99 16 | # 任务信息相关（要处理的任务） 17 | self.Function_min_task_datasize=2# 18 | self.Function_max_task_datasize = 5 # 19 | self.Function_task_computing_resource: float = 300 # 任务计算资源200cycles/bit 20 | self.Function_min_task_delay: int = 20 # 任务的最小延迟5s 21 | self.Function_max_task_delay: int = 25 # 任务的最大延迟10s 22 | 23 | 24 | 25 | # 任务队列相关（每个卸载对象自己产生的任务） 26 | self.min_rsu_task_number: int = 2 #RSU最小任务个数 27 | self.max_rsu_task_number: int = 3 #RSU最大任务个数 28 | self.min_vehicle_task_number: int = 4 #车辆最小任务个数,用于生成初始任务的个数 29 | self.max_vehicle_task_number: int = 5 #车辆最大任务个数,用于生成初始任务的个数 30 | self.min_task_datasize: float = 2 # 5 MB 每个任务的最小数据大小 31 | self.max_task_datasize: float = 4 # 10 MB 每个任务的最大数据大小 32 | 33 | # 车辆相关 34 | self.min_vehicle_speed: int = 30 #车辆行驶的最小速度 35 | self.max_vehicle_speed: int = 40 #车辆行驶的最大速度 36 | self.min_vehicle_compute_ability: float =20000 #最小计算能力25000Mcycles/s 37 | self.max_vehicle_compute_ability: float =25000 #最大计算能力30000Mcycles/s 38 | self.vehicle_number = 10 #车辆个数 39 | self.seed = 1 #随机种子 40 | self.min_vehicle_y_initial_location:float =0 #y坐标最小值 41 | self.max_vehicle_y_initial_location: float =50 #y坐标最大值 42 | self.vehicle_x_initial_location:list=[0,self.road_range]#x坐标初始值 43 | # RSU相关 44 | self.rsu_number = 3 #RSU的个数 45 | self.min_rsu_compute_ability: float = 30000 # 最小计算能力400Mcycles/s 46 | self.max_rsu_compute_ability: float = 35000 # 最大计算能力600Mcycles/s 47 | # self._rsu_x_location: dict = {"rsu_1": 200, "rsu_2": 600, "rsu_3": 1000} 48 | # self._rsu_y_location: dict = {"rsu_1": 50, "rsu_2": 50, "rsu_3": 50} 49 | 50 | # 通信相关 51 | self.rsu_range:int =400 #RSU通信距离200m 52 | self.vehicle_range: int = 200 #车辆通信距离100m 53 | self.r2v_B:float=20 #R2V带宽：10Mbps 54 | self.v2v_B:float=40#V2V带宽:20Mbps 55 | self.rsu_p:float=50 #RSU发射功率：100w 56 | self.vehicle_p:float=10 #车发射功率： 20w 57 | self.w:float=0.001 #噪声功率𝜔：0.001 W/Hz 58 | self.k:float=30 #固定损耗𝐾：20-40db取30 59 | self.theta:int=2 #路径损耗因子𝜎：2-6取2 60 | self.r2r_onehop_time:float=6#r2r一跳传输时间2s 61 | self.c2r_rate:float=0.3#C-R传输速率：2mb/s 62 | # self.cloud_compute_ability:float=1800 #cloud计算能力15000Mcycles/s 63 | self.min_transfer_rate:float=0.01 #最小传输速率：0.1mb/s 64 | self.rsu_connect_time:float=10000 #RSU之间的联通时间 65 | self.cloud_connect_time:float=10000 #R2C的连通时间 66 | #惩罚 67 | self.punishment=-200 68 | #环境相关 69 | self.action_size=(self.rsu_number+self.vehicle_number+1)** 3#动作空间 70 | self.high = np.array([np.finfo(np.float32).max for _ in range(self.rsu_number+self.vehicle_number)]) # 状态空间的最大值 71 | # high 变量将是一个包含 107 个元素的 NumPy 数组，每个元素将具有可由 32 位浮点数表示的最大有限值。 72 | self.low = np.array([0 for _ in range(self.rsu_number+self.vehicle_number)]) # 状态空间的最大值 73 | -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20230926-211815/results/params.txt: -------------------------------------------------------------------------------- 1 | ------------------ start ------------------ 2 | algo_name : A3C 3 | env_name : Multihop-V2V 4 | n_train_processes : 6 5 | max_train_ep : 150 6 | max_test_ep : 300 7 | update_interval : 5 8 | gamma : 0.98 9 | learning_rate : 0.0002 10 | hidden_dim : 256 11 | device : cuda 12 | result_path : C:\Users\Mr.yang\Desktop\Multihop-V2V-no-seed\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20230926-211815/results/ 13 | model_path : C:\Users\Mr.yang\Desktop\Multihop-V2V-no-seed\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20230926-211815/models/ 14 | save_fig : True 15 | ------------------- end ------------------- -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20230926-211815/results/train_completion_rate.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZWLab23/Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV-Networks/8a3529dc3eeb6f7afd367288134456ac2f9740e6/methods/A3C/outputs/Multihop-V2V/20230926-211815/results/train_completion_rate.npy -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20230926-211815/results/train_ma_completion_rate.npy: -------------------------------------------------------------------------------- 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旨在简化数据类的定义，减少样板代码，并提供更好的代码可读性。这对于处理大量数据对象的情况特别有用。 3 | import numpy as np 4 | 5 | 6 | @dataclasses.dataclass 7 | class VehicularEnvConfig: 8 | def __init__(self): 9 | # 道路信息 10 | self.road_range: int = 1200 # 500m 11 | self.road_width: int = 50 # 500m 12 | 13 | # 时间信息 14 | self.time_slot_start: int = 0 15 | self.time_slot_end: int = 99 16 | # 任务信息相关（要处理的任务） 17 | self.Function_min_task_datasize=2# 18 | self.Function_max_task_datasize = 5 # 19 | self.Function_task_computing_resource: float = 300 # 任务计算资源200cycles/bit 20 | self.Function_min_task_delay: int = 20 # 任务的最小延迟5s 21 | self.Function_max_task_delay: int = 25 # 任务的最大延迟10s 22 | 23 | 24 | 25 | # 任务队列相关（每个卸载对象自己产生的任务） 26 | self.min_rsu_task_number: int = 2 #RSU最小任务个数 27 | self.max_rsu_task_number: int = 3 #RSU最大任务个数 28 | self.min_vehicle_task_number: int = 4 #车辆最小任务个数,用于生成初始任务的个数 29 | self.max_vehicle_task_number: int = 5 #车辆最大任务个数,用于生成初始任务的个数 30 | self.min_task_datasize: float = 2 # 5 MB 每个任务的最小数据大小 31 | self.max_task_datasize: float = 4 # 10 MB 每个任务的最大数据大小 32 | 33 | # 车辆相关 34 | self.min_vehicle_speed: int = 30 #车辆行驶的最小速度 35 | self.max_vehicle_speed: int = 40 #车辆行驶的最大速度 36 | self.min_vehicle_compute_ability: float =20000 #最小计算能力25000Mcycles/s 37 | self.max_vehicle_compute_ability: float =25000 #最大计算能力30000Mcycles/s 38 | self.vehicle_number = 10 #车辆个数 39 | self.seed = 1 #随机种子 40 | self.min_vehicle_y_initial_location:float =0 #y坐标最小值 41 | self.max_vehicle_y_initial_location: float =50 #y坐标最大值 42 | self.vehicle_x_initial_location:list=[0,self.road_range]#x坐标初始值 43 | # RSU相关 44 | self.rsu_number = 3 #RSU的个数 45 | self.min_rsu_compute_ability: float = 25000 # 最小计算能力400Mcycles/s 46 | self.max_rsu_compute_ability: float = 30000 # 最大计算能力600Mcycles/s 47 | # self._rsu_x_location: dict = {"rsu_1": 200, "rsu_2": 600, "rsu_3": 1000} 48 | # self._rsu_y_location: dict = {"rsu_1": 50, "rsu_2": 50, "rsu_3": 50} 49 | 50 | # 通信相关 51 | self.rsu_range:int =400 #RSU通信距离200m 52 | self.vehicle_range: int = 200 #车辆通信距离100m 53 | self.r2v_B:float=20 #R2V带宽：10Mbps 54 | self.v2v_B:float=40#V2V带宽:20Mbps 55 | self.rsu_p:float=50 #RSU发射功率：100w 56 | self.vehicle_p:float=10 #车发射功率： 20w 57 | self.w:float=0.001 #噪声功率𝜔：0.001 W/Hz 58 | self.k:float=30 #固定损耗𝐾：20-40db取30 59 | self.theta:int=2 #路径损耗因子𝜎：2-6取2 60 | self.r2r_onehop_time:float=6#r2r一跳传输时间2s 61 | self.c2r_rate:float=0.25#C-R传输速率：2mb/s 62 | # self.cloud_compute_ability:float=1800 #cloud计算能力15000Mcycles/s 63 | self.min_transfer_rate:float=0.01 #最小传输速率：0.1mb/s 64 | self.rsu_connect_time:float=10000 #RSU之间的联通时间 65 | self.cloud_connect_time:float=10000 #R2C的连通时间 66 | #惩罚 67 | self.punishment=-200 68 | #环境相关 69 | self.action_size=(self.rsu_number+self.vehicle_number+1)** 3#动作空间 70 | self.high = np.array([np.finfo(np.float32).max for _ in range(self.rsu_number+self.vehicle_number)]) # 状态空间的最大值 71 | # high 变量将是一个包含 107 个元素的 NumPy 数组，每个元素将具有可由 32 位浮点数表示的最大有限值。 72 | self.low = np.array([0 for _ in range(self.rsu_number+self.vehicle_number)]) # 状态空间的最大值 73 | -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20230927-153132/results/params.txt: -------------------------------------------------------------------------------- 1 | ------------------ start ------------------ 2 | algo_name : A3C 3 | env_name : Multihop-V2V 4 | n_train_processes : 6 5 | max_train_ep : 150 6 | max_test_ep : 300 7 | update_interval : 5 8 | gamma : 0.98 9 | learning_rate : 0.0002 10 | hidden_dim : 256 11 | device : cuda 12 | result_path : C:\Users\gaopeng\Desktop\Multihop-V2V-no-seed\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20230927-153132/results/ 13 | model_path : C:\Users\gaopeng\Desktop\Multihop-V2V-no-seed\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20230927-153132/models/ 14 | save_fig : True 15 | ------------------- end ------------------- -------------------------------------------------------------------------------- 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/methods/A3C/outputs/Multihop-V2V/20230927-153927/models/a3c.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZWLab23/Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV-Networks/8a3529dc3eeb6f7afd367288134456ac2f9740e6/methods/A3C/outputs/Multihop-V2V/20230927-153927/models/a3c.pt -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20230927-153927/results/config_params.txt: -------------------------------------------------------------------------------- 1 | import dataclasses 2 | # 旨在简化数据类的定义，减少样板代码，并提供更好的代码可读性。这对于处理大量数据对象的情况特别有用。 3 | import numpy as np 4 | 5 | 6 | @dataclasses.dataclass 7 | class VehicularEnvConfig: 8 | def __init__(self): 9 | # 道路信息 10 | self.road_range: int = 1200 # 500m 11 | self.road_width: int = 50 # 500m 12 | 13 | # 时间信息 14 | self.time_slot_start: int = 0 15 | self.time_slot_end: int = 99 16 | # 任务信息相关（要处理的任务） 17 | self.Function_min_task_datasize=2# 18 | self.Function_max_task_datasize = 5 # 19 | self.Function_task_computing_resource: float = 300 # 任务计算资源200cycles/bit 20 | self.Function_min_task_delay: int = 20 # 任务的最小延迟5s 21 | self.Function_max_task_delay: int = 25 # 任务的最大延迟10s 22 | 23 | 24 | 25 | # 任务队列相关（每个卸载对象自己产生的任务） 26 | self.min_rsu_task_number: int = 2 #RSU最小任务个数 27 | self.max_rsu_task_number: int = 3 #RSU最大任务个数 28 | self.min_vehicle_task_number: int = 4 #车辆最小任务个数,用于生成初始任务的个数 29 | self.max_vehicle_task_number: int = 5 #车辆最大任务个数,用于生成初始任务的个数 30 | self.min_task_datasize: float = 2 # 5 MB 每个任务的最小数据大小 31 | self.max_task_datasize: float = 4 # 10 MB 每个任务的最大数据大小 32 | 33 | # 车辆相关 34 | self.min_vehicle_speed: int = 30 #车辆行驶的最小速度 35 | self.max_vehicle_speed: int = 40 #车辆行驶的最大速度 36 | self.min_vehicle_compute_ability: float =20000 #最小计算能力25000Mcycles/s 37 | self.max_vehicle_compute_ability: float =25000 #最大计算能力30000Mcycles/s 38 | self.vehicle_number = 10 #车辆个数 39 | self.seed = 1 #随机种子 40 | self.min_vehicle_y_initial_location:float =0 #y坐标最小值 41 | self.max_vehicle_y_initial_location: float =50 #y坐标最大值 42 | self.vehicle_x_initial_location:list=[0,self.road_range]#x坐标初始值 43 | # RSU相关 44 | self.rsu_number = 3 #RSU的个数 45 | self.min_rsu_compute_ability: float = 25000 # 最小计算能力400Mcycles/s 46 | self.max_rsu_compute_ability: float = 30000 # 最大计算能力600Mcycles/s 47 | # self._rsu_x_location: dict = {"rsu_1": 200, "rsu_2": 600, "rsu_3": 1000} 48 | # self._rsu_y_location: dict = {"rsu_1": 50, "rsu_2": 50, "rsu_3": 50} 49 | 50 | # 通信相关 51 | self.rsu_range:int =400 #RSU通信距离200m 52 | self.vehicle_range: int = 200 #车辆通信距离100m 53 | self.r2v_B:float=20 #R2V带宽：10Mbps 54 | self.v2v_B:float=40#V2V带宽:20Mbps 55 | self.rsu_p:float=50 #RSU发射功率：100w 56 | self.vehicle_p:float=10 #车发射功率： 20w 57 | self.w:float=0.001 #噪声功率𝜔：0.001 W/Hz 58 | self.k:float=30 #固定损耗𝐾：20-40db取30 59 | self.theta:int=2 #路径损耗因子𝜎：2-6取2 60 | self.r2r_onehop_time:float=6#r2r一跳传输时间2s 61 | self.c2r_rate:float=0.25#C-R传输速率：2mb/s 62 | # self.cloud_compute_ability:float=1800 #cloud计算能力15000Mcycles/s 63 | self.min_transfer_rate:float=0.01 #最小传输速率：0.1mb/s 64 | self.rsu_connect_time:float=10000 #RSU之间的联通时间 65 | self.cloud_connect_time:float=10000 #R2C的连通时间 66 | #惩罚 67 | self.punishment=-200 68 | #环境相关 69 | self.action_size=(self.rsu_number+self.vehicle_number+1)** 3#动作空间 70 | self.high = np.array([np.finfo(np.float32).max for _ in range(self.rsu_number+self.vehicle_number)]) # 状态空间的最大值 71 | # high 变量将是一个包含 107 个元素的 NumPy 数组，每个元素将具有可由 32 位浮点数表示的最大有限值。 72 | self.low = np.array([0 for _ in range(self.rsu_number+self.vehicle_number)]) # 状态空间的最大值 73 | -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20230927-153927/results/params.txt: -------------------------------------------------------------------------------- 1 | ------------------ start ------------------ 2 | algo_name : A3C 3 | env_name : Multihop-V2V 4 | n_train_processes : 6 5 | max_train_ep : 150 6 | max_test_ep : 300 7 | update_interval : 5 8 | gamma : 0.98 9 | learning_rate : 0.0002 10 | hidden_dim : 256 11 | device : cuda 12 | result_path : C:\Users\Mr.yang\Desktop\Multihop-V2V-no-seed\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20230927-153927/results/ 13 | model_path : C:\Users\Mr.yang\Desktop\Multihop-V2V-no-seed\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20230927-153927/models/ 14 | save_fig : True 15 | ------------------- end ------------------- -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20230927-153927/results/train_completion_rate.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZWLab23/Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV-Networks/8a3529dc3eeb6f7afd367288134456ac2f9740e6/methods/A3C/outputs/Multihop-V2V/20230927-153927/results/train_completion_rate.npy 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-------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20230928-110901/results/params.txt: -------------------------------------------------------------------------------- 1 | ------------------ start ------------------ 2 | algo_name : A3C 3 | env_name : Multihop-V2V 4 | n_train_processes : 6 5 | max_train_ep : 300 6 | max_test_ep : 500 7 | update_interval : 5 8 | gamma : 0.98 9 | learning_rate : 0.0002 10 | hidden_dim : 256 11 | device : cuda 12 | result_path : D:\PyCharm\PyCharmProject\pytorch\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20230928-110901/results/ 13 | model_path : D:\PyCharm\PyCharmProject\pytorch\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20230928-110901/models/ 14 | save_fig : True 15 | ------------------- end ------------------- -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | import dataclasses 2 | # 旨在简化数据类的定义，减少样板代码，并提供更好的代码可读性。这对于处理大量数据对象的情况特别有用。 3 | import numpy as np 4 | 5 | 6 | @dataclasses.dataclass 7 | class VehicularEnvConfig: 8 | def __init__(self): 9 | # 道路信息 10 | self.road_range: int = 1200 # 500m 11 | self.road_width: int = 50 # 500m 12 | 13 | # 时间信息 14 | self.time_slot_start: int = 0 15 | self.time_slot_end: int = 99 16 | # 任务信息相关（要处理的任务） 17 | self.Function_min_task_datasize=2# 18 | self.Function_max_task_datasize = 5 # 19 | self.Function_task_computing_resource: float = 300 # 任务计算资源200cycles/bit 20 | self.Function_min_task_delay: int = 20 # 任务的最小延迟5s 21 | self.Function_max_task_delay: int = 25 # 任务的最大延迟10s 22 | 23 | 24 | 25 | # 任务队列相关（每个卸载对象自己产生的任务） 26 | self.min_rsu_task_number: int = 2 #RSU最小任务个数 27 | self.max_rsu_task_number: int = 3 #RSU最大任务个数 28 | self.min_vehicle_task_number: int = 4 #车辆最小任务个数,用于生成初始任务的个数 29 | self.max_vehicle_task_number: int = 5 #车辆最大任务个数,用于生成初始任务的个数 30 | self.min_task_datasize: float = 2 # 5 MB 每个任务的最小数据大小 31 | self.max_task_datasize: float = 4 # 10 MB 每个任务的最大数据大小 32 | 33 | # 车辆相关 34 | self.min_vehicle_speed: int = 30 #车辆行驶的最小速度 35 | self.max_vehicle_speed: int = 40 #车辆行驶的最大速度 36 | self.min_vehicle_compute_ability: float =20000 #最小计算能力200Mcycles/s 37 | self.max_vehicle_compute_ability: float =25000 #最大计算能力400Mcycles/s 38 | self.vehicle_number = 10 #车辆个数 39 | self.seed = 1 #随机种子 40 | self.min_vehicle_y_initial_location:float =0 #y坐标最小值 41 | self.max_vehicle_y_initial_location: float =50 #y坐标最大值 42 | self.vehicle_x_initial_location:list=[0,self.road_range]#x坐标初始值 43 | # RSU相关 44 | self.rsu_number = 3 #RSU的个数 45 | self.min_rsu_compute_ability: float = 25000 # 最小计算能力400Mcycles/s 46 | self.max_rsu_compute_ability: float = 30000 # 最大计算能力600Mcycles/s 47 | # self._rsu_x_location: dict = {"rsu_1": 200, "rsu_2": 600, "rsu_3": 1000} 48 | # self._rsu_y_location: dict = {"rsu_1": 50, "rsu_2": 50, "rsu_3": 50} 49 | 50 | # 通信相关 51 | self.rsu_range:int =400 #RSU通信距离200m 52 | self.vehicle_range: int = 200 #车辆通信距离100m 53 | self.r2v_B:float=20 #R2V带宽：10Mbps 54 | self.v2v_B:float=40#V2V带宽:20Mbps 55 | self.rsu_p:float=50 #RSU发射功率：100w 56 | self.vehicle_p:float=10 #车发射功率： 20w 57 | self.w:float=0.001 #噪声功率𝜔：0.001 W/Hz 58 | self.k:float=30 #固定损耗𝐾：20-40db取30 59 | self.theta:int=2 #路径损耗因子𝜎：2-6取2 60 | self.r2r_onehop_time:float=8#r2r一跳传输时间2s 61 | self.c2r_rate:float=0.2#C-R传输速率：2mb/s 62 | # self.cloud_compute_ability:float=1800 #cloud计算能力15000Mcycles/s 63 | self.min_transfer_rate:float=0.01 #最小传输速率：0.1mb/s 64 | self.rsu_connect_time:float=10000 #RSU之间的联通时间 65 | self.cloud_connect_time:float=10000 #R2C的连通时间 66 | #惩罚 67 | self.punishment=-200 68 | #环境相关 69 | self.action_size=(self.rsu_number+self.vehicle_number+1)** 3#动作空间 70 | self.high = np.array([np.finfo(np.float32).max for _ in range(self.rsu_number+self.vehicle_number)]) # 状态空间的最大值 71 | # high 变量将是一个包含 107 个元素的 NumPy 数组，每个元素将具有可由 32 位浮点数表示的最大有限值。 72 | self.low = np.array([0 for _ in range(self.rsu_number+self.vehicle_number)]) # 状态空间的最大值 73 | -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20231002-214935/results/params.txt: -------------------------------------------------------------------------------- 1 | ------------------ start ------------------ 2 | algo_name : A3C 3 | env_name : Multihop-V2V 4 | n_train_processes : 6 5 | max_train_ep : 300 6 | max_test_ep : 500 7 | update_interval : 5 8 | gamma : 0.98 9 | learning_rate : 0.0002 10 | hidden_dim : 256 11 | device : cuda 12 | result_path : D:\PyCharm\PyCharmProject\pytorch\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20231002-214935/results/ 13 | model_path : D:\PyCharm\PyCharmProject\pytorch\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20231002-214935/models/ 14 | save_fig : True 15 | ------------------- end ------------------- 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-------------------------------------------------------------------------------- 1 | import dataclasses 2 | # 旨在简化数据类的定义，减少样板代码，并提供更好的代码可读性。这对于处理大量数据对象的情况特别有用。 3 | import numpy as np 4 | 5 | 6 | @dataclasses.dataclass 7 | class VehicularEnvConfig: 8 | def __init__(self): 9 | # 道路信息 10 | self.road_range: int = 1200 # 500m 11 | self.road_width: int = 50 # 500m 12 | 13 | # 时间信息 14 | self.time_slot_start: int = 0 15 | self.time_slot_end: int = 99 16 | # 任务信息相关（要处理的任务） 17 | self.Function_min_task_datasize=2# 18 | self.Function_max_task_datasize = 5 # 19 | self.Function_task_computing_resource: float = 300 # 任务计算资源200cycles/bit 20 | self.Function_min_task_delay: int = 20 # 任务的最小延迟5s 21 | self.Function_max_task_delay: int = 25 # 任务的最大延迟10s 22 | 23 | 24 | 25 | # 任务队列相关（每个卸载对象自己产生的任务） 26 | self.min_rsu_task_number: int = 2 #RSU最小任务个数 27 | self.max_rsu_task_number: int = 3 #RSU最大任务个数 28 | self.min_vehicle_task_number: int = 4 #车辆最小任务个数,用于生成初始任务的个数 29 | self.max_vehicle_task_number: int = 5 #车辆最大任务个数,用于生成初始任务的个数 30 | self.min_task_datasize: float = 2 # 5 MB 每个任务的最小数据大小 31 | self.max_task_datasize: float = 4 # 10 MB 每个任务的最大数据大小 32 | 33 | # 车辆相关 34 | self.min_vehicle_speed: int = 30 #车辆行驶的最小速度 35 | self.max_vehicle_speed: int = 40 #车辆行驶的最大速度 36 | self.min_vehicle_compute_ability: float =24000 #最小计算能力200Mcycles/s 37 | self.max_vehicle_compute_ability: float =25000 #最大计算能力400Mcycles/s 38 | self.vehicle_number = 10 #车辆个数 39 | self.seed = 1 #随机种子 40 | self.min_vehicle_y_initial_location:float =0 #y坐标最小值 41 | self.max_vehicle_y_initial_location: float =50 #y坐标最大值 42 | self.vehicle_x_initial_location:list=[0,self.road_range]#x坐标初始值 43 | # RSU相关 44 | self.rsu_number = 3 #RSU的个数 45 | self.min_rsu_compute_ability: float = 25000 # 最小计算能力400Mcycles/s 46 | self.max_rsu_compute_ability: float = 30000 # 最大计算能力600Mcycles/s 47 | # self._rsu_x_location: dict = {"rsu_1": 200, "rsu_2": 600, "rsu_3": 1000} 48 | # self._rsu_y_location: dict = {"rsu_1": 50, "rsu_2": 50, "rsu_3": 50} 49 | 50 | # 通信相关 51 | self.rsu_range:int =400 #RSU通信距离200m 52 | self.vehicle_range: int = 200 #车辆通信距离100m 53 | self.r2v_B:float=20 #R2V带宽：10Mbps 54 | self.v2v_B:float=40#V2V带宽:20Mbps 55 | self.rsu_p:float=50 #RSU发射功率：100w 56 | self.vehicle_p:float=10 #车发射功率： 20w 57 | self.w:float=0.001 #噪声功率𝜔：0.001 W/Hz 58 | self.k:float=30 #固定损耗𝐾：20-40db取30 59 | self.theta:int=2 #路径损耗因子𝜎：2-6取2 60 | self.r2r_onehop_time:float=8#r2r一跳传输时间2s 61 | self.c2r_rate:float=0.2#C-R传输速率：2mb/s 62 | # self.cloud_compute_ability:float=1800 #cloud计算能力15000Mcycles/s 63 | self.min_transfer_rate:float=0.01 #最小传输速率：0.1mb/s 64 | self.rsu_connect_time:float=10000 #RSU之间的联通时间 65 | self.cloud_connect_time:float=10000 #R2C的连通时间 66 | #惩罚 67 | self.punishment=-200 68 | #环境相关 69 | self.action_size=(self.rsu_number+self.vehicle_number+1)** 3#动作空间 70 | self.high = np.array([np.finfo(np.float32).max for _ in range(self.rsu_number+self.vehicle_number)]) # 状态空间的最大值 71 | # high 变量将是一个包含 107 个元素的 NumPy 数组，每个元素将具有可由 32 位浮点数表示的最大有限值。 72 | self.low = np.array([0 for _ in range(self.rsu_number+self.vehicle_number)]) # 状态空间的最大值 73 | -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20231005-192015/results/params.txt: -------------------------------------------------------------------------------- 1 | ------------------ start ------------------ 2 | algo_name : A3C 3 | env_name : Multihop-V2V 4 | n_train_processes : 6 5 | max_train_ep : 300 6 | max_test_ep : 500 7 | update_interval : 5 8 | gamma : 0.98 9 | learning_rate : 0.0002 10 | hidden_dim : 256 11 | device : cuda 12 | result_path : D:\PyCharm\PyCharmProject\pytorch\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20231005-192015/results/ 13 | model_path : D:\PyCharm\PyCharmProject\pytorch\Multihop-V2V-no-seed\methods\A3C/outputs/Multihop-V2V/20231005-192015/models/ 14 | save_fig : True 15 | ------------------- end ------------------- 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-------------------------------------------------------------------------------- 1 | ------------------ start ------------------ 2 | algo_name : A3C 3 | env_name : Multihop-V2V 4 | n_train_processes : 6 5 | max_train_ep : 300 6 | max_test_ep : 500 7 | update_interval : 5 8 | gamma : 0.98 9 | learning_rate : 0.0002 10 | hidden_dim : 256 11 | device : cuda 12 | result_path : D:\PyCharm\PyCharmProject\pytorch\Multihop-V2V-no-script\methods\A3C/outputs/Multihop-V2V/20231120-093833/results/ 13 | model_path : D:\PyCharm\PyCharmProject\pytorch\Multihop-V2V-no-script\methods\A3C/outputs/Multihop-V2V/20231120-093833/models/ 14 | save_fig : True 15 | ------------------- end ------------------- -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20231120-093833/results/train_completion_rate.npy: -------------------------------------------------------------------------------- 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: Multihop-V2V 4 | n_train_processes : 6 5 | max_train_ep : 200 6 | max_test_ep : 300 7 | update_interval : 5 8 | gamma : 0.98 9 | learning_rate : 0.0002 10 | hidden_dim : 256 11 | device : cuda 12 | result_path : D:\PyCharm\PyCharmProject\pytorch\Multihop-V2V-no-script\methods\A3C/outputs/Multihop-V2V/20231123-164321/results/ 13 | model_path : D:\PyCharm\PyCharmProject\pytorch\Multihop-V2V-no-script\methods\A3C/outputs/Multihop-V2V/20231123-164321/models/ 14 | save_fig : True 15 | ------------------- end ------------------- -------------------------------------------------------------------------------- /methods/A3C/outputs/Multihop-V2V/20231123-164321/results/train_completion_rate.npy: -------------------------------------------------------------------------------- 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parent_path = os.path.dirname(curr_path) # 父路径 4 | # print(curr_path) 5 | 6 | sys.path.append(parent_path) # 添加路径到系统路径 7 | # parent_path_1 = os.path.dirname(parent_path) 8 | # sys.path.append(parent_path_1) 9 | # print(parent_path) 10 | 11 | import numpy as np 12 | import torch 13 | import argparse 14 | from methods.A3C.a3c import ActorCritic 15 | import matplotlib.pyplot as plt 16 | import seaborn as sns 17 | from env.utils import plot_rewards, save_args,plot_completion_rate 18 | from env.utils import save_results_1, make_dir 19 | 20 | from env import environment 21 | from torch.distributions import Categorical 22 | import torch.nn.functional as F 23 | import torch.optim as optim 24 | import torch.multiprocessing as mp 25 | import datetime 26 | from env.config import VehicularEnvConfig 27 | 28 | 29 | 30 | def get_args(): 31 | """ Hyperparameters 32 | """ 33 | curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间 34 | parser = argparse.ArgumentParser(description="hyperparameters") 35 | parser.add_argument('--algo_name',default='A3C',type=str,help="name of algorithm") 36 | #算法名称：A2C 37 | parser.add_argument('--env_name',default='Multihop-V2V',type=str,help="name of environment") 38 | #环境名称：CartPole-v0 39 | parser.add_argument('--n_train_processes',default=6,type=int,help="numbers of environments") 40 | #创建8个独立的并行运行环境 41 | parser.add_argument('--max_train_ep',default=200,type=int,help="episodes of training") 42 | #训练回合数 43 | parser.add_argument('--max_test_ep',default=300,type=int,help="episodes of testing") 44 | #测试回合数 45 | parser.add_argument('--update_interval', default=5, type=int, help="展开轨迹数") 46 | # 展开轨迹数 47 | parser.add_argument('--gamma',default=0.98,type=float,help="discounted factor") 48 | #折扣因子 49 | parser.add_argument('--learning_rate',default=0.0002,type=float,help="learning rate") 50 | #学习率 51 | parser.add_argument('--hidden_dim',default=256,type=int) 52 | parser.add_argument('--device', default="cuda" if torch.cuda.is_available() else "cpu", type=str, help="cpu or cuda") 53 | parser.add_argument('--result_path', default=curr_path + "/outputs/" + parser.parse_args().env_name + \ 54 | '/' + curr_time + '/results/') 55 | parser.add_argument('--model_path', default=curr_path + "/outputs/" + parser.parse_args().env_name + \ 56 | '/' + curr_time + '/models/') # path to save models 57 | parser.add_argument('--save_fig', default=True, type=bool, help="if save figure or not") 58 | #隐藏层的神经元数目 59 | args = parser.parse_args() 60 | args.device = torch.device( 61 | "cuda" if torch.cuda.is_available() else "cpu") # check GPU 62 | return args 63 | 64 | def env_agent_config(cfg, seed=1): 65 | ''' 创建环境和智能体 66 | ''' 67 | env = environment.RoadState() # 创建环境 68 | n_states = env.observation_space.shape[0] # 状态维度 69 | n_actions = env.action_space.n 70 | # print(f"n states: {n_states}, n actions: {n_actions}") 71 | agent =ActorCritic(n_states, n_actions, cfg.hidden_dim) # 创建智能体 72 | # if seed != 0: # 设置随机种子 73 | # torch.manual_seed(seed) 74 | # env.seed(seed) 75 | # np.random.seed(seed) 76 | return env, agent 77 | 78 | 79 | def train(cfg, env, agent,global_model,rank): 80 | agent.load_state_dict(global_model.state_dict()) 81 | optimizer = optim.Adam(global_model.parameters(), lr=cfg.learning_rate) 82 | 83 | for n_epi in range(cfg.max_train_ep): 84 | done = False 85 | state,function = env.reset() 86 | while not done: 87 | state_lst, action_lst, reward_lst = [], [], [] 88 | for t in range(cfg.update_interval):#展开轨迹,收集经验 89 | prob = agent.actor(torch.from_numpy(state).float()) 90 | dist = Categorical(prob) 91 | action = dist.sample().item() 92 | # 根据概率分布dist进行采样，返回一个采样的动作。 93 | # 由于动作是离散的，因此item()方法用于将采样的动作从张量中提取出来，转换成普通的Python整数类型。 94 | next_state, reward, done,next_function,_,_,_,_= env.step(action,function) 95 | 96 | state_lst.append(state) 97 | action_lst.append([action]) 98 | # r_lst.append(r/100.0) 99 | reward_lst.append(reward) 100 | state = next_state 101 | function=next_function 102 | if done: 103 | break 104 | 105 | # state_final = torch.tensor(next_state, dtype=torch.float) 106 | # final_state轨迹的最后一个时间步 107 | final_state = torch.tensor(next_state, dtype=torch.float) 108 | V = 0.0 if done else agent.critic(final_state).item() 109 | td_target_lst = [] 110 | for reward in reward_lst[::-1]: 111 | V = cfg.gamma * V + reward 112 | td_target_lst.append([V]) 113 | td_target_lst.reverse() # 将列表中的元素顺序进行反转 114 | 115 | state_lst=np.array(state_lst, dtype=np.float32) 116 | action_lst = np.array(action_lst, dtype=np.int64) 117 | td_target_lst = np.array(td_target_lst, dtype=np.float32) 118 | 119 | state_batch, action_batch, td_target = torch.tensor(state_lst, dtype=torch.float), \ 120 | torch.tensor(action_lst), torch.tensor(td_target_lst) 121 | advantage = td_target - agent.critic(state_batch) 122 | 123 | action_prob = agent.actor(state_batch,softmax_dim=1) 124 | all_action_prob = action_prob.gather(1, action_batch) 125 | loss = -torch.log(all_action_prob) * advantage.detach() + \ 126 | F.smooth_l1_loss(agent.critic(state_batch), td_target.detach()) 127 | 128 | optimizer.zero_grad() 129 | loss.mean().backward() 130 | for global_param, local_param in zip(global_model.parameters(), agent.parameters()): 131 | # 这是一个for循环，它通过zip函数将全局模型的参数和对应的局部模型的参数一一对应起来 132 | global_param._grad = local_param.grad 133 | # 在每次迭代中，将局部模型的参数梯度（local_param.grad）传递给全局模型的参数（global_param._grad）。 134 | # 这样，全局模型的参数会受到来自多个进程局部模型的梯度影响，实现了梯度的累积。 135 | optimizer.step() 136 | # 在每次迭代中，使用优化器来更新全局模型的参数。因为全局模型的参数累积了多个进程局部模型的梯度， 137 | # 所以此步骤会根据累积的梯度来更新全局模型的参数。 138 | agent.load_state_dict(global_model.state_dict()) 139 | # 在每次迭代结束后，将全局模型的参数拷贝回局部模型，确保每个进程的局部模型保持与全局模型相同的参数状态， 140 | # 以便下一次迭代时继续与环境交互。 141 | env.close() 142 | print("Training process {} reached maximum episode.".format(rank)) 143 | 144 | def test_global_model(cfg, env,global_model): 145 | rewards_plot = [] 146 | ma_rewards_plot = [] 147 | offloading_vehicle_number_plot = [] 148 | offloading_rsu_number_plot = [] 149 | offloading_cloud_number_plot = [] 150 | completion_rate_plot=[] 151 | ma_completion_rate_plot = [] 152 | for n_epi in range(cfg.max_test_ep): 153 | rewards=0 154 | steps=0 155 | done = False 156 | offloading_vehicle_number = 0 157 | offloading_rsu_number = 0 158 | offloading_cloud_number = 0 159 | complete_number =0 160 | # completion_rate=0 161 | state ,function= env.reset() 162 | while not done: 163 | prob = global_model.actor(torch.from_numpy(state).float()) 164 | action = Categorical(prob).sample().item() 165 | next_state, reward, done,next_function,offloading_vehicle,offloading_rsu,offloading_cloud,complete = env.step(action,function) 166 | state = next_state 167 | function=next_function 168 | steps+=1 169 | rewards += reward 170 | offloading_vehicle_number+=offloading_vehicle 171 | offloading_rsu_number+=offloading_rsu 172 | offloading_cloud_number+=offloading_cloud 173 | complete_number+=complete 174 | rewards_plot.append(rewards) 175 | offloading_vehicle_number_plot.append(offloading_vehicle_number) 176 | offloading_rsu_number_plot.append(offloading_rsu_number) 177 | offloading_cloud_number_plot.append( offloading_cloud_number) 178 | completion_rate=complete_number/(VehicularEnvConfig().rsu_number*(VehicularEnvConfig().time_slot_end+1)) 179 | completion_rate_plot.append(completion_rate) 180 | 181 | print("# episode :{}, steps : {}, rewards : {}, complete : {}, vehicle : {}, rsu : {}, cloud : {}" 182 | .format(n_epi+1,steps, rewards, 183 | completion_rate,offloading_vehicle_number,offloading_rsu_number,offloading_cloud_number)) 184 | # time.sleep(1) 185 | 186 | if ma_rewards_plot: 187 | ma_rewards_plot.append(0.9 * ma_rewards_plot[-1] + 0.1 * rewards) 188 | else: 189 | ma_rewards_plot.append(rewards) 190 | 191 | if ma_completion_rate_plot: 192 | ma_completion_rate_plot.append(0.9 * ma_completion_rate_plot[-1] + 0.1 * completion_rate) 193 | else: 194 | ma_completion_rate_plot.append(completion_rate) 195 | 196 | 197 | res_dic_rewards = {'rewards': rewards_plot, 'ma_rewards': ma_rewards_plot} 198 | res_dic_completion_rate = {'completion_rate': completion_rate_plot, 'ma_completion_rate': ma_completion_rate_plot} 199 | if not os.path.exists(cfg.result_path): 200 | os.makedirs(cfg.result_path) 201 | save_results_1(res_dic_rewards, tag='train', 202 | path=cfg.result_path) 203 | save_results_1(res_dic_completion_rate, tag='train', 204 | path=cfg.result_path) 205 | plot_rewards(res_dic_rewards['rewards'], res_dic_rewards['ma_rewards'], cfg, tag="train") 206 | plot_completion_rate(res_dic_completion_rate['completion_rate'], res_dic_completion_rate['ma_completion_rate'], cfg, tag="train") 207 | env.close() 208 | 209 | 210 | 211 | 212 | if __name__ == '__main__': 213 | cfg=get_args() 214 | make_dir(cfg.result_path, cfg.model_path) 215 | env,global_model =env_agent_config(cfg) 216 | global_model.share_memory() 217 | # 创建一个队列用于存储结果数据 218 | result_queue = mp.Queue() 219 | processes = [] 220 | # 创建一个空列表processes，用于存储所有的进程。 221 | for rank in range(cfg.n_train_processes + 1): 222 | #通过for循环，遍历range(n_train_processes + 1)，其中n_train_processes + 1是训练进程的数量再加1（用于测试进程）。 223 | if rank == 0: 224 | _, agent = env_agent_config(cfg) 225 | #当rank为0时，创建一个进程p，目标函数是test，并传入global_model作为参数。 226 | p = mp.Process(target=test_global_model, args=(cfg,env,global_model)) 227 | else: 228 | #当rank不为0时，创建一个进程p，目标函数是train，并传入global_model和rank作为参数。 229 | _,agent=env_agent_config(cfg) 230 | p = mp.Process(target=train, args=(cfg, env, agent,global_model,rank)) 231 | p.start() 232 | #启动进程p，开始执行对应的训练或测试任务。 233 | processes.append(p) 234 | #将进程p添加到processes列表中，以便稍后等待所有进程完成。 235 | for p in processes: 236 | #使用for循环，等待所有进程完成。通过调用p.join()，主程序会等待每个进程执行完毕后再继续执行。 237 | p.join() 238 | 239 | save_args(cfg) # 保存参数 240 | global_model.save(path=cfg.model_path) # save model 241 | -------------------------------------------------------------------------------- /methods/DQN/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ZWLab23/Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV-Networks/8a3529dc3eeb6f7afd367288134456ac2f9740e6/methods/DQN/README.md -------------------------------------------------------------------------------- /methods/DQN/dqn.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding=utf-8 3 | ''' 4 | @Author: John 5 | @Email: johnjim0816@gmail.com 6 | @Date: 2020-06-12 00:50:49 7 | @LastEditor: John 8 | LastEditTime: 2022-07-13 00:08:18 9 | @Discription: 10 | @Environment: python 3.7.7 11 | ''' 12 | '''off-policy 13 | ''' 14 | 15 | import torch 16 | import torch.nn as nn 17 | import torch.nn.functional as F 18 | import torch.optim as optim 19 | import random 20 | import math 21 | import numpy as np 22 | 23 | class MLP(nn.Module): 24 | def __init__(self, n_states,n_actions,hidden_dim=128): 25 | """ 初始化q网络，为全连接网络 26 | n_states: 输入的特征数即环境的状态维度 27 | n_actions: 输出的动作维度 28 | """ 29 | super(MLP, self).__init__() 30 | self.fc1 = nn.Linear(n_states, hidden_dim) # 输入层 31 | self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层 32 | self.fc3 = nn.Linear(hidden_dim, n_actions) # 输出层 33 | 34 | def forward(self, x): 35 | # 各层对应的激活函数 36 | x = F.relu(self.fc1(x)) 37 | x = F.relu(self.fc2(x)) 38 | return self.fc3(x) 39 | 40 | class ReplayBuffer: 41 | def __init__(self, capacity): 42 | self.capacity = capacity # 经验回放的容量 43 | self.buffer = [] # 缓冲区 44 | self.position = 0 45 | 46 | def push(self, state, action, reward, next_state, done): 47 | ''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition) 48 | ''' 49 | if len(self.buffer) < self.capacity: 50 | self.buffer.append(None) 51 | self.buffer[self.position] = (state, action, reward, next_state, done) 52 | self.position = (self.position + 1) % self.capacity 53 | 54 | def sample(self, batch_size): 55 | batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移 56 | state, action, reward, next_state, done = zip(*batch) # 解压成状态，动作等 57 | return state, action, reward, next_state, done 58 | 59 | def __len__(self): 60 | ''' 返回当前存储的量 61 | ''' 62 | return len(self.buffer) 63 | 64 | class DQN: 65 | def __init__(self, n_states,n_actions,cfg): 66 | 67 | self.n_actions = n_actions # 总的动作个数 68 | self.device = cfg.device # 设备，cpu或gpu等 69 | self.gamma = cfg.gamma # 奖励的折扣因子 70 | # e-greedy策略相关参数 71 | self.frame_idx = 0 # 用于epsilon的衰减计数 72 | self.epsilon = lambda frame_idx: cfg.epsilon_end + \ 73 | (cfg.epsilon_start - cfg.epsilon_end) * \ 74 | math.exp(-1. * frame_idx / cfg.epsilon_decay) 75 | self.batch_size = cfg.batch_size 76 | self.policy_net = MLP(n_states,n_actions).to(self.device) 77 | self.target_net = MLP(n_states,n_actions).to(self.device) 78 | for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # 复制参数到目标网路targe_net 79 | target_param.data.copy_(param.data) 80 | self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器 81 | self.memory = ReplayBuffer(cfg.memory_capacity) # 经验回放 82 | 83 | def choose_action(self, state): 84 | ''' 选择动作 85 | ''' 86 | self.frame_idx += 1 87 | if random.random() > self.epsilon(self.frame_idx): 88 | with torch.no_grad(): 89 | state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(dim=0) 90 | q_values = self.policy_net(state) 91 | action = q_values.max(1)[1].item() # 选择Q值最大的动作 92 | else: 93 | action = random.randrange(self.n_actions) 94 | return action 95 | def update(self): 96 | if len(self.memory) < self.batch_size: # 当memory中不满足一个批量时，不更新策略 97 | return 98 | # 从经验回放中(replay memory)中随机采样一个批量的转移(transition) 99 | # print('updating') 100 | 101 | state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample( 102 | self.batch_size) 103 | state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float) 104 | action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1) 105 | reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float) 106 | next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float) 107 | done_batch = torch.tensor(np.float32(done_batch), device=self.device) 108 | q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch) # 计算当前状态(s_t,a)对应的Q(s_t, a) 109 | next_q_values = self.target_net(next_state_batch).max(1)[0].detach() # 计算下一时刻的状态(s_t_,a)对应的Q值 110 | # 计算期望的Q值，对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward 111 | expected_q_values = reward_batch + self.gamma * next_q_values * (1-done_batch) 112 | loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) # 计算均方根损失 113 | # 优化更新模型 114 | self.optimizer.zero_grad() 115 | loss.backward() 116 | for param in self.policy_net.parameters(): # clip防止梯度爆炸 117 | param.grad.data.clamp_(-1, 1) 118 | self.optimizer.step() 119 | 120 | def save(self, path): 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from env import environment 23 | from env.config import VehicularEnvConfig 24 | 25 | from env.utils import plot_rewards, save_args, plot_completion_rate 26 | from env.utils import save_results_1, make_dir 27 | 28 | 29 | def get_args(): 30 | """ Hyperparameters 31 | """ 32 | curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # Obtain current time 33 | parser = argparse.ArgumentParser(description="hyperparameters") 34 | parser.add_argument('--algo_name',default='DQN',type=str,help="name of algorithm") 35 | parser.add_argument('--env_name',default='Multihop-V2V',type=str,help="name of environment") 36 | parser.add_argument('--train_eps',default=300,type=int,help="episodes of training") 37 | parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") 38 | parser.add_argument('--gamma',default=0.95,type=float,help="discounted factor") 39 | parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon") 40 | parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") 41 | parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon") 42 | parser.add_argument('--lr',default=0.00005,type=float,help="learning rate") 43 | parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity") 44 | parser.add_argument('--batch_size',default=128,type=int)#64 45 | parser.add_argument('--target_update',default=4,type=int) 46 | parser.add_argument('--hidden_dim',default=256,type=int) 47 | parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \ 48 | '/' + curr_time + '/results/' ) 49 | parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \ 50 | '/' + curr_time + '/models/' ) # path to save models 51 | parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not") 52 | args = parser.parse_args() 53 | args.device = torch.device( 54 | "cuda" if torch.cuda.is_available() else "cpu") # check GPU 55 | return args 56 | 57 | def env_agent_config(cfg,seed=1): 58 | ''' 创建环境和智能体 59 | ''' 60 | env = environment.RoadState() # 创建环境 61 | n_states = env.observation_space.shape[0] # 状态维度 62 | n_actions = env.action_space.n # 动作维度 63 | print(f"n states: {n_states}, n actions: {n_actions}") 64 | agent = DQN(n_states,n_actions, cfg) # 创建智能体 65 | # if seed !=0: # 设置随机种子 66 | # torch.manual_seed(seed) 67 | # env.seed(seed) 68 | # np.random.seed(seed) 69 | return env, agent 70 | 71 | def train(cfg, env, agent): 72 | ''' Training 73 | ''' 74 | print('Start training!') 75 | print(f'Env:{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}') 76 | rewards_plot = [] 77 | ma_rewards_plot = [] 78 | offloading_vehicle_number_plot = [] 79 | offloading_rsu_number_plot = [] 80 | offloading_cloud_number_plot = [] 81 | completion_rate_plot=[] 82 | ma_completion_rate_plot = [] 83 | 84 | for i_ep in range(cfg.train_eps): 85 | rewards = 0 # 记录一回合内的奖励 86 | steps = 0 87 | offloading_vehicle_number = 0 88 | offloading_rsu_number = 0 89 | offloading_cloud_number = 0 90 | complete_number =0 91 | state,function = env.reset() # 重置环境，返回初始状态 92 | while True: 93 | steps+= 1 94 | action = agent.choose_action(state) # 选择动作 95 | next_state, reward, done,next_function,offloading_vehicle,offloading_rsu,offloading_cloud,complete = env.step(action,function) # 更新环境，返回transition 96 | agent.memory.push(state, action, reward, 97 | next_state, done) # 保存transition 98 | state = next_state # 更新下一个状态 99 | function = next_function 100 | agent.update() # 更新智能体 101 | rewards += reward # 累加奖励 102 | offloading_vehicle_number+=offloading_vehicle 103 | offloading_rsu_number+=offloading_rsu 104 | offloading_cloud_number+=offloading_cloud 105 | complete_number+=complete 106 | if done: 107 | break 108 | if (i_ep + 1) % cfg.target_update == 0: # 智能体目标网络更新 109 | agent.target_net.load_state_dict(agent.policy_net.state_dict()) 110 | 111 | rewards_plot.append(rewards) 112 | offloading_vehicle_number_plot.append(offloading_vehicle_number) 113 | offloading_rsu_number_plot.append(offloading_rsu_number) 114 | offloading_cloud_number_plot.append(offloading_cloud_number) 115 | completion_rate = complete_number / ( 116 | VehicularEnvConfig().rsu_number * (VehicularEnvConfig().time_slot_end + 1)) 117 | completion_rate_plot.append(completion_rate) 118 | print("# episode :{}, steps : {}, rewards : {}, complete : {}, vehicle : {}, rsu : {}, cloud : {}" 119 | .format(i_ep + 1, steps, rewards, 120 | completion_rate, offloading_vehicle_number, offloading_rsu_number, offloading_cloud_number)) 121 | if ma_rewards_plot: 122 | ma_rewards_plot.append(0.9 * ma_rewards_plot[-1] + 0.1 * rewards) 123 | else: 124 | ma_rewards_plot.append(rewards) 125 | 126 | if ma_completion_rate_plot: 127 | ma_completion_rate_plot.append(0.9 * ma_completion_rate_plot[-1] + 0.1 * completion_rate) 128 | else: 129 | ma_completion_rate_plot.append(completion_rate) 130 | 131 | res_dic_rewards = {'rewards': rewards_plot, 'ma_rewards': ma_rewards_plot} 132 | res_dic_completion_rate = {'completion_rate': completion_rate_plot, 133 | 'ma_completion_rate': ma_completion_rate_plot} 134 | if not os.path.exists(cfg.result_path): 135 | os.makedirs(cfg.result_path) 136 | save_results_1(res_dic_rewards, tag='train', 137 | path=cfg.result_path) 138 | save_results_1(res_dic_completion_rate, tag='train', 139 | path=cfg.result_path) 140 | plot_rewards(res_dic_rewards['rewards'], res_dic_rewards['ma_rewards'], cfg, tag="train") 141 | plot_completion_rate(res_dic_completion_rate['completion_rate'], res_dic_completion_rate['ma_completion_rate'], 142 | cfg, tag="train") 143 | env.close() 144 | 145 | 146 | 147 | if __name__ == "__main__": 148 | cfg = get_args() 149 | # 训练 150 | env, agent = env_agent_config(cfg) 151 | train(cfg, env, agent) 152 | -------------------------------------------------------------------------------- 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当前文件所在绝对路径 3 | parent_path = os.path.dirname(curr_path) # 父路径 4 | print(curr_path) 5 | sys.path.append(parent_path) # 添加路径到系统路径 6 | # parent_path_1 = os.path.dirname(parent_path) 7 | # sys.path.append(parent_path_1) 8 | 9 | import torch 10 | import argparse 11 | 12 | from env.utils import plot_rewards, save_args, plot_completion_rate 13 | from env.utils import save_results_1, make_dir 14 | from env import environment 15 | 16 | import datetime 17 | from env.config import VehicularEnvConfig 18 | from methods.Greedy.greedy_tasksize import Greedy 19 | 20 | 21 | def get_args(): 22 | """ Hyperparameters 23 | """ 24 | curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间 25 | parser = argparse.ArgumentParser(description="hyperparameters") 26 | parser.add_argument('--algo_name',default='greedy',type=str,help="name of algorithm") 27 | #算法名称：A2C 28 | parser.add_argument('--env_name',default='Multihop-V2V',type=str,help="name of environment") 29 | parser.add_argument('--train_eps', default=300, type=int, help="episodes of training") 30 | parser.add_argument('--test_eps', default=200, type=int, help="episodes of testing") 31 | parser.add_argument('--result_path', 32 | default=curr_path + "/outputs/" + parser.parse_args().env_name + '/' + curr_time + '/results/') 33 | parser.add_argument('--model_path', # path to save models 34 | default=curr_path + "/outputs/" + parser.parse_args().env_name + '/' + curr_time + '/models/') 35 | parser.add_argument('--save_fig', default=True, type=bool, help="if save figure or not") 36 | args = parser.parse_args() 37 | args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check GPU 38 | return args 39 | 40 | 41 | 42 | def train(cfg, env, agent): 43 | """ Training """ 44 | print('Start training!') 45 | print(f'Env:{cfg.env_name}, A{cfg.algo_name}, 设备：{cfg.device}') 46 | rewards_plot = [] 47 | ma_rewards_plot = [] 48 | offloading_vehicle_number_plot = [] 49 | offloading_rsu_number_plot = [] 50 | offloading_cloud_number_plot = [] 51 | completion_rate_plot=[] 52 | ma_completion_rate_plot = [] 53 | for n_epi in range(cfg.train_eps): 54 | rewards=0 55 | steps=0 56 | done = False 57 | offloading_vehicle_number = 0 58 | offloading_rsu_number = 0 59 | offloading_cloud_number = 0 60 | complete_number =0 61 | state,function= env.reset() 62 | while not done: 63 | action = agent.choose_action( state,function) 64 | 65 | next_state, reward, done,next_function,offloading_vehicle,offloading_rsu,offloading_cloud,complete = env.step(action,function) 66 | state = next_state 67 | function=next_function 68 | steps+=1 69 | rewards += reward 70 | offloading_vehicle_number+=offloading_vehicle 71 | offloading_rsu_number+=offloading_rsu 72 | offloading_cloud_number+=offloading_cloud 73 | complete_number+=complete 74 | rewards_plot.append(rewards) 75 | offloading_vehicle_number_plot.append(offloading_vehicle_number) 76 | offloading_rsu_number_plot.append(offloading_rsu_number) 77 | offloading_cloud_number_plot.append( offloading_cloud_number) 78 | completion_rate = complete_number / (VehicularEnvConfig().rsu_number * (VehicularEnvConfig().time_slot_end + 1)) 79 | completion_rate_plot.append(completion_rate) 80 | print("# episode :{}, steps : {}, rewards : {}, complete : {}, vehicle : {}, rsu : {}, cloud : {}" 81 | .format(n_epi+1,steps, rewards, 82 | completion_rate,offloading_vehicle_number,offloading_rsu_number,offloading_cloud_number)) 83 | # time.sleep(1) 84 | 85 | if ma_rewards_plot: 86 | ma_rewards_plot.append(0.9 * ma_rewards_plot[-1] + 0.1 * rewards) 87 | else: 88 | ma_rewards_plot.append(rewards) 89 | 90 | if ma_completion_rate_plot: 91 | ma_completion_rate_plot.append(0.9 * ma_completion_rate_plot[-1] + 0.1 * completion_rate) 92 | else: 93 | ma_completion_rate_plot.append(completion_rate) 94 | 95 | 96 | res_dic_rewards = {'rewards': rewards_plot, 'ma_rewards': ma_rewards_plot} 97 | res_dic_completion_rate = {'completion_rate': completion_rate_plot, 'ma_completion_rate': ma_completion_rate_plot} 98 | if not os.path.exists(cfg.result_path): 99 | os.makedirs(cfg.result_path) 100 | save_results_1(res_dic_rewards, tag='train', 101 | path=cfg.result_path) 102 | save_results_1(res_dic_completion_rate, tag='train', 103 | path=cfg.result_path) 104 | plot_rewards(res_dic_rewards['rewards'], res_dic_rewards['ma_rewards'], cfg, tag="train") 105 | plot_completion_rate(res_dic_completion_rate['completion_rate'], res_dic_completion_rate['ma_completion_rate'], cfg, tag="train") 106 | env.close() 107 | 108 | 109 | 110 | 111 | 112 | if __name__ == "__main__": 113 | cfg = get_args() 114 | # 训练 115 | env = environment.RoadState() 116 | agent = Greedy() 117 | train(cfg, env, agent) 118 | 119 | 120 | 121 | #20000-250000 122 | #8 --------------------------------------------------------------------------------