gym-csgo

Counter-Strike: Global Offensive environment for OpenAI Gym on Linux

:bangbang: Never use this connecting to official/online game servers! Never cheat! It might get you banned.

:bangbang: Consider creating a separate throwaway steam account for experimenting with this environment.

Prerequisites

To use the gym environment, steam for Linux with Counter-Strike: Global Offensive installed needs to be available.

As the native (Linux, using OpenGL) version of Counter-Strike: Global Offensive does not get hardware acceleration in virtual X servers like Xvfb or Xephyr, it is necessary to run the game in compatibility mode, to get reasonable performance (frames per second) in the gym environment: Using the steam client, in the Properties of Counter-Strike: Global Offensive navigate to Compatibility and check Force the use of a specific Steam Play compatibility tool and select Proton 5.13-6 (others might work but are not tested) from the drop-down menu below.

As of 1 October 2021 Proton 6.3-6 is longer available in the steam client. Using 6.3-7 the game keeps crashing just after startup, thus 5.13-6 seems to be the best option for now.

With the recent addition of experimental Vulkan support, it might actually be possible to run the native Linux version of Counter-Strike with sufficient performance. Please try this by adding -vulkan to your command line.

It should be possible to launch Counter-Strike: Global Offensive (App ID 730) from the terminal (this might take some time, especially the first start after updating or setting the compatibility):

steam -applaunch 730 -insecure -untrusted -novid -nojoy

Game State Integration

Counter-Strike: Global Offensive Game State Integration is necessary to communicate information about the current game state to the python interface. This needs to be set up in the game configurations: Copy the game state integration configuration file from the cfg directory of the repository into the cfg directory of the Counter-Strike: Global Offensive installation.

To find out more about the Counter-Strike: Global Offensive Game State Integration and its configuration look at the Valve Developer Community.

Virtual Display

The gym environment executes the game on a virtual X server display, either inside a window on the pre-existing X display (Xephyr) or invisible in the background (Xvfb). To install the required packages on Ubuntu:

sudo apt install xvfb xserver-xephyr

Installation

Note: This package is still in early stages of development, installing might miss dependencies or does not work at all.

pip install --upgrade gym-csgo

Basic Usage

Running a Deathmatch (game mode) environment with default configuration and random actions per step until it is done (the match is done after 10 minutes):

# gym_csgo registers the envs (to gym.make(...))
import gym_csgo
# Gym environments
import gym

# Open new environment context (automatically closes env at end of scope)
with gym.make('csgo_dm-v0') as env:
    # Reset the environment
    env.reset()
    # Env is not done yet
    done = False
    # Until the environment is done
    while not done:
        # Get random action from environment
        action = env.action_space.sample()
        # Execute the random action and collect observation
        obs, rew, done, info = env.step(action)

Demo Actors

Programs showing demo actors in the environment are provided in the gym_csgo.demo subpackage. There are random and noop actors which sample random actions from the environment's action space or do no action at all: These might be useful for testing the environment in general (esp. functionality, startup, graphics, etc.) or experiment with configuration options which can passed to the game. Start a random actor playing a deathmatch and show the frames per second to evaluate the performance:

python -m gym_csgo.demo.random csgo_dm-v0 de_dust2 +cl_showfps 1

A special case is the manual actor which allows to actually play the game through a pygame display interacting with the gym environment which itself wraps the game on the virtual display. This is merely a technical demonstration but might as well be suited as a starting point for collecting human demonstration data. Play a game of casual game mode on the map train (the pygame actor will be available once the match starts after the warmup period):

python -m gym_csgo.demo.manual csgo_casual-v0 de_train