DooT 2 is a learning project with the ultimate goal of teaching a deep learning agent to play a video game from pixel input only using a single prosumer-grade GPU. The agent should be capable of generalizing for previously unseen environments (within the same game) from a very sparse reward input (ideally level completion / game end only). The chosen methodology is abstract in a sense that it is applicable to any environment conforming to the standard reinforcement learning observation - action - state update loop.
The original Doom was chosen as the environment due to various desirable properties:
- Very long sequences: 35 tics * 60 s * 10 min = 21000 frames for a medium length level, necessitating observation compression and compact state representation
- Partial state observation: in FPS games the player can only see what's directly in front of them in addition to various player status indicators such as health and ammo
- Very long cause-and-effect delays (for example picking a keycard will grant access to an area much later) requiring long-term memory and capability of making connections between events separated by significant amount of timesteps
- The game can be run effectively on CPU, allowing GPU to be fully utilized for training the ML model
- Pre-existing tools for procedurally generating infinite number of levels of varying difficulty
Tested with CMake 3.25.1, gcc, g++ 10.3.0 and 11.3.0
- Install dependencies (Ubuntu Linux 22.04):
$ sudo apt install libsdl2-dev libeigen3-dev libopencv-dev
- Clone the repository, checkout the
developbranch for the latest changes:
$ git clone git@github.com:Lehdari/DooT2.git
$ cd DooT2/
$ git checkout develop
- Clone the submodules:
$ git submodule update --init --recursive
- Build (note that PyTorch will be built when invoking CMake the first time, this will take quite a while depending on your hardware):
$ mkdir build
$ cd build
$ cmake .. -DCMAKE_BUILD_TYPE=Release -GNinja
$ ninja -j0 doot2
- Run
$ ./doot2