from myDQN.DQN import DQN
import tensorflow as tf
import numpy as np
import pandas as pd
from entity import *

n_actions = 2
n_features = 3

max_x = 50
max_y = 50
max_time = 144

def train():
    data = load('../train_data/train_data.pkl')
    print("数据量：",sum(len(i) for i in data))
    step = 0
    RL = DQN(n_actions,n_features)
    for d in data:
        for match in d:
            s_x = match.driver.x / max_x
            s_y = match.driver.y / max_y
            s_time = match.order.order_time / max_time
            _s_x = match.order.to_x / max_x
            _s_y = match.order.to_y / max_y
            _s_time = match.order.arrive_time / max_time
            travel_time = match.order.travel_time
            reward = match.money
            RL.store_transition((s_x,s_y,s_time),0,reward,travel_time,(_s_x,_s_y,_s_time))

            if (step > 200) and (step % 10 == 0):
                RL.learn()
            step += 1

    RL.plot_cost()






if __name__ == '__main__':
    train()

    pass