DOOM is available on all platforms, but the PC version lets you use console commands to access various cheats and power-ups to make the game easier/more fun. These range from God mode to revealing all the secrets of a map.

I'm going to list all the console commands available for the 2016 version of DOOM and what they do.

Right-click on the Chocolate Doom archive and extract its files into the same folder as the shareware episode or where you installed the full game. Open the folder and start chocolate-doom.exe, which should launch the game. If you want to configure sound, keys or other features, open chocolate-setup.exe instead. Playing Doom on the Windows XP, Vista, 7, 8, and 10 operating systems can be problematic. Games based on the Doom engine originally ran under MS-DOS. Modern Windows versions have limited to no support for DOS programs, and the Doom and other Doom engine games may run poorly, have serious issues (sound support is especially affected), or not run at all, especially if the system is 64-bit. DOOM is available on all platforms, but the PC version lets you use console commands to access various cheats and power-ups to make the game easier/more fun. These range from God mode to revealing all the secrets of a map. Play in the legendary game DOOM in browser. (Uploaded by: @evilution) Powered by js-dos.com. Doom II: Hell on Earth is an award winning first-person shooter video game and the second title of id Software's Doom franchise. It was originally released for MS-DOS computers in 1994 and Macintosh computers in 1995. The Macintosh version was developed in Austin, Texas by developers such as Brett Butler.

Console Commands

Before starting I want to remind you that only PC versions of the game can do this. It will not work on Xbox One or Playstation 4. You will also not be able to earn any achievements with any of these commands activated, so make a separate save file if this is important to you.

To open console commands, press, 'Ctrl Alt ~' at the same time on your keyboard. Type the below codes in the console to activate them.

ida - Gives all suit mods and slots.

iddt - Reveals all the secrets of a map, including unexplored areas and items.

idfa - Gives max upgrades to suit health, armor, and ammo.

idgk - Turns on Glory Kills Only where you can only kill an enemy by performing a Glory Kill on them.

idka - Gives all weapons and weapon mods. Also, this gives the fully upgraded versions of the mods.

iddqd - Turns on God mode. Health will slowly drain in Nightmare modes and this does not affect environmental hazards.

idkfa - Gives all of the ida, idfa, and idka options at once.

The other ones are for when you only want to turn on certain ones.

After playing through the campaign, or even before, why not give a few of these console commands a try? Sometimes it is fun to just go though a game killing everything and feeling unstoppable.

It is also helpful if you want to try out certain weapons or upgrades before spending the token to upgrade them.

With recent scientific advancements in Deep Learning, Artificial Intelligence and Neural Networks, as well as steadily evolving tools such as Tensorflow, Pytorch, and Keras, writing, testing and optimizing your own Neural Networks is now easier than ever before. Amazed by the results of the Vizdoom competition, I decided to implement my own simple Doom AI from scratch. Having no practical knowledge of Deep Learning, I decided to share my journey from an empty Python project to an (at least decently) working Doom AI.

Prerequisites

First of all, I want to cover this topic more from an engineering than a Data Science background, so don’t expect any deep elaborations on the theory behind Neural Networks. A great introduction to this topic is the Artificial Intelligence A-Z course on Udemy. If you prefer books, check out the Hands-On Machine Learning with Scikit-Learn and TensorFlow book on Amazon.

Additionally, feel free to check out the following papers:
– AI handbook
– Reinforcement Learning I: Introduction
– Asynchronous Methods for Deep Reinforcement Learning

Software

To train the Doom agent, we will use the Pytorch library. Since we have to move a lot of data through the Artificial Neural Network (ANN) we are going to create, training on a GPU is mandatory. To set up your PC, check out Tuatinis amazing blog post on setting up an environment for deep learning.
Besides that, you will need Python and the pip package manager as well as Vizdoom to play the game and interact with the ANN. To install Vizdoom, please follow the instructions on their githubpage.

Setting up the project

To run pre-configured scenarios for training an AI, Vizdoom provides a bunch of .cfg files we can use right away. To make use of them, I copied the Vizdoom scenarios directory into an empty Python project. Among these scenarios are some fairly easy (Basic, Deadly Corridor) and some more challenging ones (My way home, Deathmatch). We will try to create a generic model so we can easily switch between all these scenarios.
Next, I created a src directory, that will contain all source code for the project. In there, I split up the project into three further parts: doom, which contains our client for Vizdoom and at the same time provides an interface to be used by our AI. Besides that, I created a models directory, which will contain the different Deep learning models, as well as a utils directory for logging, argument-parsing and preprocessing.
In the src directory itself, we will need an empty __init.py__ and a main.py file as starting point for our project.
If you followed along until this point, you should have a project structure that looks like this:

You can also clone the full repository from doom-ai.

Playing the game

Before we start creating our AI, let’s get used to the Vizdoom API. To do so, we’ll create a simple client that allows us to play the game ourselves! To keep our interface to the Vizdoom API in one place, I created a doom_trainer.py file inside the doom directory:

The create_actions method will later be used to generate all the actions our AI can choose from. To play the game ourselves, we can leave this method empty for now. The DoomTrainer class contains methods to initialize Vizdoom, play as human, create new episodes and to let our AI make actions. I will go into more detail about these methods later. For now, let’s focus on the __init__ and play_human methods:
__init__ takes a params object that contains all information required to train the AI. The first two lines of this method create a new DoomGame instance and load the scenario based on the path from the params object. Then, if we pass the ‘human’ model, we set the game mode to Mode.SPECTATOR, which will hand all control to the game to the player.
The play_human method sets up a loop that retrieves all user input and forwards it to Vizdoom.

To use these new methods and hook up our future AI with the doom_trainer, let’s add a new game.py file and add a play method to it:

The first two lines will initialize Pytorch to run on our GPU. After that, we check the provided model from the provided parameters and then call the according method. Beside the human model we will implement now, I added a model for A3C. We will look at that later.

With that said, let’s implement the play_human method, so we can finally start interacting with Vizdoom:

That’s all the code we need! We instantiate our new class and call its start_game and play_human methods.

We will call this new method from a new main.py file defined in the root directory of the project:

Here, we simply get the command line parameters via the parse_arguments method and pass them on to the play method we just created. Since we are playi

Play Doom On Cmd