Unreal environments for reinforcement learning
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Gym-UnrealCV: Realistic virtual worlds for visual reinforcement learning


This project integrates Unreal Engine with OpenAI Gym for visual reinforcement learning based on UnrealCV. In this project, you can run your RL algorithms in various realistic UE4 environments easily without any knowledge of Unreal Engine and UnrealCV. The framework of this project is shown as below:


  • UnrealCV is the basic bridge between Unreal Engine and OpenAI Gym.
  • OpenAI Gym is a toolkit for developing RL algorithm, compatible with most of numerical computation library, such as Tensorflow or Theano.

search1 search2

Snapshots of RL based visual navigation for object searching and obstacle avoidance.



  • UnrealCV
  • Gym
  • CV2
  • Matplotlib
  • Numpy
  • Docker(Optional)
  • Nvidia-Docker(Optional)

We recommend you to use anaconda to install and manage your python environment. CV2 is used for images processing, like extracting object mask and bounding box.Matplotlib is used for visualization.

Install Gym-UnrealCV

It is easy to install gym-unrealcv, just run

git clone https://github.com/zfw1226/gym-unrealcv.git
cd gym-unrealcv
pip install -e . 

While installing gym-unrealcv, dependencies including OpenAI Gym, unrealcv, numpy and matplotlib are installed. Opencv is should be installed additionally. If you use anaconda,you can run

conda update conda
conda install --channel menpo opencv

Prepare Unreal Environment

You need prepare an unreal environment to run the demo envirnment. You can do it by running the script RealisticRendering.sh

sh RealisticRendering.sh

or manually download the RealisticRendering env from this link, then unzip and move it to the UnrealEnv directory.

Note that you can download more environments from UnrealCV Model Zoo.

There are two ways to launch the unreal environment in gym-unrealcv, called docker-based and docker-free. The docker-based way depends on docker and nvidia-docker. The docker-free way launches the env binary directly. The docker-based way promise more stable unrealcv connection and support to run the env parallelly. On the contrast, the docker-free way only support running an unreal environment in the same time. So the docker-based way is highly recommended to get better experience. You can learn to install and use the docker-based way in this page.

Note that the default config runs in the Docker-free way.


Run a random agent

Once gym-unrealcv is installed successfully, you will see that your agent is walking randomly in first-person view to find a door, after you run:

cd example/random
python random_agent.py -e 'Search-RrDoorDiscrete-v0'

It will take a few minutes for the image to pull if you runs environment based on docker at the first time. After that, if all goes well,a pre-defined gym environment Search-RrDoorDiscrete-v0 will be launched. And then you will see that your agent is moving around the realistic room randomly.

We list the pre-defined environments in this page, for object searching and active object tracking.


We provide a set of tutorials to help you get started with Gym-UnrealCV.

1. Modify the pre-defined environment

You can follow the modify_env_tutorial to modify the configuration of the pre-defined environment.

2. Add a new unreal environment

You can follow the add_new_env_tutorial to add new unreal environment for your RL task.

3. Training a reinforcement learning agent

Besides, we also provide examples, such as DQN and DDPG, to demonstrate how to train agent in gym-unrealcv.


If you use Gym-UnrealCV in your academic research, we would be grateful if you could cite it as follow:

    author = {Fangwei Zhong, Weichao Qiu, Tingyun Yan, Alan Yuille, Yizhou Wang},
    title = {Gym-UnrealCV: Realistic virtual worlds for visual reinforcement learning},
    howpublished={Web Page},
    url = {https://github.com/unrealcv/gym-unrealcv},
    year = {2017}


if you have any suggestion or interested in using Gym-UnrealCV, get in touch at zfw1226@gmail.com.