main.py

import os
from environment.environment_discrete import EnvironmentDiscrete
from environment.environment_continous import EnvironmentContinous
import pandas as pd
from rl_algorithms.deep_q_learning import DeepQLearningAgent
from rl_algorithms.ddpg import DDPGAgent
import time

def load_dataset():
    script_dir = os.path.dirname(__file__)
    file_path = os.path.join(script_dir, './dataset/data.csv')
    df = pd.read_csv(file_path)

    return df

def split_dataset(df, split_index):
    df_train = df[df.index <= split_index]
    df_test = df[df.index > split_index]

    return df_train, df_test

def main():
    #dataset contains power injection of nodes
    #df = load_dataset()
    #df_train, df_test = split_dataset(df, 998)

    #environment_discrete = EnvironmentDiscrete()
    #agent = DeepQLearningAgent(environment_discrete)

    environment_continous = EnvironmentContinous()
    agent = DDPGAgent(environment_continous)

    n_episodes = 20000
    print('agent training started')
    t1 = time.time()
    #agent.train(n_episodes)
    t2 = time.time()
    print ('agent training finished in', t2-t1)


    node_ids = range(1, 15) #1, 2,... 14
    values = [0.0 for i in range(len(node_ids))]
    initial_disturbance_dict = dict(zip(node_ids, values))
    initial_disturbance_dict[14] = 0.79
    test_disturbance_list = [initial_disturbance_dict]
    agent.test(test_disturbance_list)

if __name__ == '__main__':
    main()