CellFreeCLCA_RL
A. Directory Path
data
├── 4.4.5.2 # Topology with 4 UE/4 AP/5 File/2 Cache
│ ├── BF # Brute Force result (pkl and png)
│ ├── Topology # Topology (pkl and png)
│ ├── Preview # In Training phase, we can preview the performance result in this folder, the result will update each 1000 time slot
│ ├── Model # After training is done, the trained model will be saved in this folder. In single actor case, there are 2 model (actor and critic).
│ ├── EvaluationPhase # After evaluation is done, plotHistory() will plot the performnace result and save the corresponding figures in this folder
│ └── EVSampledPolicy # In evaluation phase, we will take a policy snapshot in half length of evaluation, the snapshot will save in this folder and be plotted via plot_UE_BS_distribution_Cache()
└── 10.5.20.2
└── (same as above)
B. Code mainbody:
- newSimulate.py
- newENV.py
- newDDPG.py
The code is run under Pytorch:1.8.0.dev20201027 and Python 3.8.5.
C. Dependency:
- newSimulate.py <- newENV.py
- newSimulate.py <- newDDPG.py
D. Standard model training and evaluation procedure:(run newSimulate.py)
- Train model using trainModel()
- Evaluate trained model using evaluateModel()
- plot evaluation result using plotHistory(), the function can plot EE/HR/TP/Psys/MCAP/MCCPU
- plot policy sampled in evaluation phase using plot_UE_BS_distribution_Cache()
E. Generate arbitrary topology:
In newENV(), declare class BS by set parameter loadENV=False. For example,
env = BS(nBS=10,nUE=5,nMaxLink=3,nFile=20,nMaxCache=2,loadENV = False,SEED=0,obsIdx=1)
It will produce a new topology in data/10.5.20.2/Topology in pkl form and png preview
F. Derve Brute Force (BF) result (Direct example)
In newENV(), run
env=BS(nBS=10,nUE=5,nMaxLink=nMaxLink,nFile=20,nMaxCache=2,loadENV = True,SEED=0)
env.getOptEE_BF(isSave=True)
G. Paper Link https://ieeexplore.ieee.org/document/9625449
评论