draft.py

import numpy as np
import gymnasium as gym

import environment 

from stable_baselines3 import PPO, DQN
import os
import time

def main():
    process = [] 
    num_s = 1
    process.append(num_s+1)
    num_ot = 5
    process.append(num_ot+1)
    num_sh_a = 3
    process.append(num_sh_a+1)
    num_sh_b = 3
    process.append(num_sh_b+1)
    num_sh_c = 3
    process.append(num_sh_c+1)
    num_m_a = 3
    process.append(num_m_a+1)
    num_m_b = 2
    process.append(num_m_b+1)
    num_p_a = 4
    process.append(num_p_a+1)
    num_p_b = 5
    process.append(num_p_b+1)
    num_p_c = 4
    process.append(num_p_c+1)
    num_ds_a = 7
    process.append(num_ds_a+1)
    num_ds_b = 7
    process.append(num_ds_b+1)
    num_ds_c = 7
    process.append(num_ds_c+1)

    case = []
    for i in range(15):
        case.append(2)
        
    space = [process, case]
    activities = 16

    env = environment.BusinessProcessEnv(space, activities)
    env.reset()

    models_dir = f"models/{int(time.time())}/"
    logdir = f"logs/{int(time.time())}/"

    if not os.path.exists(models_dir):
        os.makedirs(models_dir)

    if not os.path.exists(logdir):
        os.makedirs(logdir)

    # model = PPO('MultiInputPolicy', env, verbose=1, tensorboard_log=logdir)
    model = DQN('MultiInputPolicy', env, verbose=1, tensorboard_log=logdir)

    TIMESTEPS = 500000
    iters = 0
    while True:
        iters += 1
        model.learn(total_timesteps=TIMESTEPS, reset_num_timesteps=False, tb_log_name=f"DQN")
        model.save(f"{models_dir}/{TIMESTEPS*iters}")

    


if __name__ == "__main__":
    main()