https://github.com/shakenes/vizdoomgym

OpenAI Gym wrapper for ViZDoom enviroments
https://github.com/shakenes/vizdoomgym

openai-gym openai-gym-environments reinforcement-learning vizdoom

Last synced: 3 months ago
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OpenAI Gym wrapper for ViZDoom enviroments

• Host: GitHub
• URL: https://github.com/shakenes/vizdoomgym
• Owner: shakenes
• License: mit
• Created: 2018-04-26T13:54:03.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-07-15T12:24:45.000Z (over 3 years ago)
• Last Synced: 2024-06-28T04:32:26.479Z (4 months ago)
• Topics: openai-gym, openai-gym-environments, reinforcement-learning, vizdoom
• Language: Python
• Homepage:
• Size: 180 KB
• Stars: 64
• Watchers: 4
• Forks: 27
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-AI-books - vizdoomgym - based AI Research Platform for Reinforcement Learning from Raw Visual Information) enviroments. (Training ground)

README

        
# ViZDoomGym ![](https://github.com/shakenes/vizdoomgym/workflows/perform_tests/badge.svg)

This is a wrapper to use [ViZDoom](https://github.com/mwydmuch/ViZDoom "ViZDoom repository"), a "Doom based AI Research Platform for Reinforcement Learning from Raw Visual Information" together with [OpenAI Gym](https://github.com/openai/gym "OpenAI Gym repository").

There is a branch with an alternative reward system for the Health Gathering scenario (each collected health pack yields +1 reward)

## Installation

```

sudo apt-get install cmake libboost-all-dev libgtk2.0-dev libsdl2-dev python-numpy

git clone https://github.com/shakenes/vizdoomgym.git

cd vizdoomgym

pip install -e .

```

## Usage

Use one of the environments (see list below for all available envs):

```

import gym

import vizdoomgym

env = gym.make('VizdoomBasic-v0', **kwargs)

# use like a normal Gym environment

state = env.reset()

state, reward, done, info = env.step(env.action_space.sample())

env.render()

env.close()

```

### Possible observations

It is possible to get the depth buffer, the labels buffer, the player's position and viewing angle and the player's health as additional observations. If more than one observation is provided, the observations are returned as a list of numpy array (or floats, respectively). 

```

env = gym.make('VizdoomBasic-v0, depth=True, labels=True, position=True, health=True)

```

### List of available environments

```

VizdoomBasic-v0

VizdoomCorridor-v0

VizdoomDefendCenter-v0

VizdoomDefendLine-v0

VizdoomHealthGathering-v0

VizdoomMyWayHome-v0

VizdoomPredictPosition-v0

VizdoomTakeCover-v0

VizdoomDeathmatch-v0

VizdoomHealthGatheringSupreme-v0

```

[Detailed information about the environments](https://github.com/shakenes/vizdoomgym/blob/master/vizdoomgym/envs/scenarios/README.md)

## Future plans

- Support for more GameVariables such as health through kwargs

- Provide more options for the observations (player position, health...)

- Use a dictionary for all the observations

Ideas for new features and of course PRs are always welcome!