A more interesting self-driving AI - Python Plays GTA V





Welcome to part 13 of the self-driving GTA 5 AI with Python series. I was finding the scooter to be far too boring to create training data for, so I have decided to upgrade to a sportbike instead. Also, after seeing the results of the scooter after such a small amount of training data, I was too certain it would work just fine to train the AI to stay in between the lanes. With certain success, that's too boring as well, we want a challenge. So, instead, I am bringing back in traffic, and I have decided to make an AI that can navigate traffic at high speeds. That's a bit more of a challenge.

This time, I trained the AI with over 300,000 training samples after balancing, and the AI appears to be okay. There are a lot of little issues, but, overall, I am pleased. The main issue I have right now is with the controls themselves, I really want PID control, so this is going to be the next thing I put my focus towards. I think making this change will produce much better results, but we'll see. For now, the code used:

# create_training_data.py

import numpy as np
from grabscreen import grab_screen
import cv2
import time
from getkeys import key_check
import os


def keys_to_output(keys):
    '''
    Convert keys to a ...multi-hot... array

    [A,W,D] boolean values.
    '''
    output = [0,0,0]
    
    if 'A' in keys:
        output[0] = 1
    elif 'D' in keys:
        output[2] = 1
    else:
        output[1] = 1
    return output


file_name = 'training_data.npy'

if os.path.isfile(file_name):
    print('File exists, loading previous data!')
    training_data = list(np.load(file_name))
else:
    print('File does not exist, starting fresh!')
    training_data = []


def main():

    for i in list(range(4))[::-1]:
        print(i+1)
        time.sleep(1)


    paused = False
    while(True):

        if not paused:
            # 800x600 windowed mode
            screen = grab_screen(region=(0,40,800,640))
            last_time = time.time()
            screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
            screen = cv2.resize(screen, (160,120))
            # resize to something a bit more acceptable for a CNN
            keys = key_check()
            output = keys_to_output(keys)
            training_data.append([screen,output])
            
            if len(training_data) % 1000 == 0:
                print(len(training_data))
                np.save(file_name,training_data)

        keys = key_check()
        if 'T' in keys:
            if paused:
                paused = False
                print('unpaused!')
                time.sleep(1)
            else:
                print('Pausing!')
                paused = True
                time.sleep(1)


main()

The create_training_data.py file is used to record your actions and frames from the game, saving them to a numpy file.

# balance_data.py

import numpy as np
import pandas as pd
from collections import Counter
from random import shuffle

train_data = np.load('training_data.npy')

df = pd.DataFrame(train_data)
print(df.head())
print(Counter(df[1].apply(str)))

lefts = []
rights = []
forwards = []

shuffle(train_data)

for data in train_data:
    img = data[0]
    choice = data[1]

    if choice == [1,0,0]:
        lefts.append([img,choice])
    elif choice == [0,1,0]:
        forwards.append([img,choice])
    elif choice == [0,0,1]:
        rights.append([img,choice])
    else:
        print('no matches')


forwards = forwards[:len(lefts)][:len(rights)]
lefts = lefts[:len(forwards)]
rights = rights[:len(forwards)]

final_data = forwards + lefts + rights
shuffle(final_data)

np.save('training_data.npy', final_data)

The balance_data.py file is used to take the training data, and balance it evenly across actions to take. Every time file sizes were ~100MB, I saved the balanced file with an ID appended to it, and started a new one. This is so we can just load in chunks of 100MB at a time when training the network.

# train_model.py

import numpy as np
from alexnet import alexnet

WIDTH = 160
HEIGHT = 120
LR = 1e-3
EPOCHS = 10
MODEL_NAME = 'pygta5-car-fast-{}-{}-{}-epochs-300K-data.model'.format(LR, 'alexnetv2',EPOCHS)

model = alexnet(WIDTH, HEIGHT, LR)

hm_data = 22
for i in range(EPOCHS):
    for i in range(1,hm_data+1):
        train_data = np.load('training_data-{}-balanced.npy'.format(i))

        train = train_data[:-100]
        test = train_data[-100:]

        X = np.array([i[0] for i in train]).reshape(-1,WIDTH,HEIGHT,1)
        Y = [i[1] for i in train]

        test_x = np.array([i[0] for i in test]).reshape(-1,WIDTH,HEIGHT,1)
        test_y = [i[1] for i in test]

        model.fit({'input': X}, {'targets': Y}, n_epoch=1, validation_set=({'input': test_x}, {'targets': test_y}), 
            snapshot_step=500, show_metric=True, run_id=MODEL_NAME)

        model.save(MODEL_NAME)



# tensorboard --logdir=foo:C:/path/to/log

This trains the model for us, assuming you have the alexnet model. You can use whatever model you want, however. If you don't have the alexnet model file:

# alexnet.py

""" AlexNet.
Applying 'Alexnet' to Oxford's 17 Category Flower Dataset classification task.
References:
    - Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton. ImageNet
    Classification with Deep Convolutional Neural Networks. NIPS, 2012.

Links:
    - [AlexNet Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
"""

import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from tflearn.layers.normalization import local_response_normalization

def alexnet(width, height, lr):
    network = input_data(shape=[None, width, height, 1], name='input')
    network = conv_2d(network, 96, 11, strides=4, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 256, 5, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 384, 3, activation='relu')
    network = conv_2d(network, 256, 3, activation='relu')
    network = max_pool_2d(network, 3, strides=2)
    network = local_response_normalization(network)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 4096, activation='tanh')
    network = dropout(network, 0.5)
    network = fully_connected(network, 3, activation='softmax')
    network = regression(network, optimizer='momentum',
                         loss='categorical_crossentropy',
                         learning_rate=lr, name='targets')

    model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                        max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='log')

    return model

Once trained, you can test the model with:

# test_model.py

import numpy as np
from grabscreen import grab_screen
import cv2
import time
from directkeys import PressKey,ReleaseKey, W, A, S, D
from alexnet import alexnet
from getkeys import key_check

import random

WIDTH = 160
HEIGHT = 120
LR = 1e-3
EPOCHS = 10
MODEL_NAME = 'pygta5-car-fast-{}-{}-{}-epochs-300K-data.model'.format(LR, 'alexnetv2',EPOCHS)

t_time = 0.09

def straight():
##    if random.randrange(4) == 2:
##        ReleaseKey(W)
##    else:
    PressKey(W)
    ReleaseKey(A)
    ReleaseKey(D)

def left():
    PressKey(W)
    PressKey(A)
    #ReleaseKey(W)
    ReleaseKey(D)
    #ReleaseKey(A)
    time.sleep(t_time)
    ReleaseKey(A)

def right():
    PressKey(W)
    PressKey(D)
    ReleaseKey(A)
    #ReleaseKey(W)
    #ReleaseKey(D)
    time.sleep(t_time)
    ReleaseKey(D)
    
model = alexnet(WIDTH, HEIGHT, LR)
model.load(MODEL_NAME)

def main():
    last_time = time.time()
    for i in list(range(4))[::-1]:
        print(i+1)
        time.sleep(1)

    paused = False
    while(True):
        
        if not paused:
            # 800x600 windowed mode
            #screen =  np.array(ImageGrab.grab(bbox=(0,40,800,640)))
            screen = grab_screen(region=(0,40,800,640))
            print('loop took {} seconds'.format(time.time()-last_time))
            last_time = time.time()
            screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
            screen = cv2.resize(screen, (160,120))

            prediction = model.predict([screen.reshape(160,120,1)])[0]
            print(prediction)

            turn_thresh = .75
            fwd_thresh = 0.70

            if prediction[1] > fwd_thresh:
                straight()
            elif prediction[0] > turn_thresh:
                left()
            elif prediction[2] > turn_thresh:
                right()
            else:
                straight()

        keys = key_check()

        # p pauses game and can get annoying.
        if 'T' in keys:
            if paused:
                paused = False
                time.sleep(1)
            else:
                paused = True
                ReleaseKey(A)
                ReleaseKey(W)
                ReleaseKey(D)
                time.sleep(1)

main()       

If you would like the training data and the trained model, you're in luck, here you go! Self-driving training data and a trained model

The next tutorial:






  • Reading game frames in Python with OpenCV - Python Plays GTA V
  • OpenCV basics - Python Plays GTA V
  • Direct Input to Game - Python Plays GTA V
  • Region of Interest for finding lanes - Python Plays GTA V
  • Hough Lines - Python Plays GTA V
  • Finding Lanes for our self driving car - Python Plays GTA V
  • Self Driving Car - Python Plays GTA V
  • Next steps for Deep Learning self driving car - Python Plays GTA V
  • Training data for self driving car neural network- Python Plays GTA V
  • Balancing neural network training data- Python Plays GTA V
  • Training Self-Driving Car neural network- Python Plays GTA V
  • Testing self-driving car neural network- Python Plays GTA V
  • A more interesting self-driving AI - Python Plays GTA V
  • Object detection with Tensorflow - Self Driving Cars in GTA
  • Determining other vehicle distances and collision warning - Self Driving Cars in GTA
  • Getting the Agent a Vehicle- Python Plays GTA V
  • Acquiring a Vehicle for the Agent - Python Plays GTA V