A Deep Reinforcement Learning-Based Scheme for Solving Multiple Knapsack Problems
This project shows the official codes that used in A Deep Reinforcement Learning-Based Scheme for Solving Multiple Knapsack Problems
Appl. Sci. 2022, 12(6), 3068; https://doi.org/10.3390/app12063068
I used it in spyder IDE, and the scripts are as follow creating item and knapsack instances
runfile('C:/yourdirectory/RI.py', wdir='C:/yourdirectory',args='1000 50 3 10 80') runfile('C:/yourdirectory/LI.py', wdir='C:/yourdirectory',args='1000 50 3 10 10') runfile('C:/yourdirectory/QI.py', wdir='C:/yourdirectory',args='1000 50 1 10 20')
train and test (in here, the train file should be hard coded in a3c mode)
runfile('C:/yourdirectory/train.py', wdir='C:/yourdirectory',args='1000 0.0001 50 1000 5 0.9999999 6 4 0') runfile('C:/yourdirectory/test.py', wdir='C:/yourdirectory',args='1000 0.0001 50 1 5 0.9999999 6 4')
comparison algorithm To run, gurobi, you need a license
runfile('C:/yourdirectory/random_sol_knap.py', wdir='C:/yourdirectory',args='1000 0.001 50 1 1 0.99') runcell(0, 'C:/yourdirectory/gurobi_op_mul.py') runfile('C:/yourdirectory/ffh_mul.py', wdir='C:/yourdirectory',args='1000 0.001 50 1 1 0.99')
I will delete the redundant part ASAP, but the code works well in here.