![neon drive steam neon drive steam](https://neondrivegame.com/images/web_logo1.png)
This means better-performing scenarios will run for a longer duration, accumulating a larger return. In this task, rewards are +1 for every incremental timestep and the environment terminates if the car hits the obstacle. In order, the respective images are normal input image, image converted to grayscale, cropped image with triangle threshold, and lastly the cropped image with triangle threshold and all the binary pixels inverted.Īs the agent observes the current state of the environment and chooses an action, the environment transitions to a new state, and also returns a reward that indicates the consequences of the action. You can follow the steps of this process in the following images: Finally, we resize the final image to 160x90 pixels using area interpolation and invert all of the binary pixels. After that, we applied the triangle threshold function to transform the image to black and white. We cut 53.84% of the upper pixels, 20% of the lower pixels, and 20% of the left and right pixels. With the BGR screen saved, we applied a color filter available in the OpenCV module to transform everything to grayscale.
![neon drive steam neon drive steam](https://cdn.cloudflare.steamstatic.com/steam/apps/487240/header.jpg)
Through the mss module, the screen was captured and transformed into a NumPy array variable. The image processing performed in this work is quite simple, but it is very important for the overall functioning of the algorithm.
![neon drive steam neon drive steam](https://img.youtube.com/vi/97TSGg-rOMI/0.jpg)
In effect, the network is trying to predict the expected return of taking each action given the current input. It has three outputs, representing, and where is the input to the network. Our model will be a convolutional neural network that takes in the difference between the current and previous screen patches. This will allow the agent to take the velocity of the obstacles into account from one image.
#Neon drive steam Patch
Strictly speaking, we will present the state as the difference between the current screen patch and the previous one. Since we cannot render multiple environments at the same time, we need a lot of training time. By using the only image our task becomes much more difficult. Neural networks can usually solve tasks just by looking at the location, so let's use a piece of the screen centered on the car as an input.
#Neon drive steam install
Pip3 install -r requirements.txt Deep Q-Network