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Unreal environments for reinforcement learning
| Date | Stars |
|---|---|
| 2026-07-31 | 449 |
| 2026-08-01 | 449 |
| 2026-08-02 | 449 |
| 2026-08-03 | 449 |
| 2026-08-06 | 449 |
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Gym-UnrealCV: Realistic virtual worlds for visual reinforcement learning
===
> 🚀 **Attention:** We highly recommend taking a look at *[UnrealZoo](https://github.com/UnrealZoo/unrealzoo-gym)*, which offers a comprehensive collection of environments and new features for Embodied AI research.
# Introduction
**This project integrates Unreal Engine with OpenAI Gym for visual reinforcement learning based on [UnrealCV](http://unrealcv.org/).**
In this project, you can run (Multi-Agent) Reinforcement Learning algorithms in various realistic UE4 environments easily without any knowledge of Unreal Engine and UnrealCV.
A number of environments have been released for robotic vision tasks, including `Active object tracking`, `Searching for objects`, and `Robot arm control`.
<table>
<tr>
<td>
<figure>
<img src="./doc/figs/track/urbancity.gif" width="240" height="180">
<figcaption>Tracking in UrbanCity with distractors</figcaption>
</figure>
</td>
<td>
<figure>
<img src="./doc/figs/track/garden.gif" width="240" height="180">
<figcaption>Tracking in Garden</figcaption>
</figure>
</td>
<td>
<figure>
<img src="./doc/figs/track/snow.gif" width="240" height="180">
<figcaption>Tracking in SnowForest</figcaption>
</figure>
</td>
</tr>
<tr>
<td>
<figure>
<img src="./doc/figs/track/garage.gif" width="240" height="180">
<figcaption>Tracking in Garage with distractors</figcaption>
</figure>
</td>
<td>
<figure>
<img src="./doc/figs/search/search2.gif" width="240" height="180">
<figcaption>Searching in RealisticRoom</figcaption>
</figure>
</td>
<td>
<figure>
<img src="./doc/figs/arm/reach-virtual.gif" width="240" height="180">
<figcaption>Robot Arm Control</figcaption>
</figure>
</td>
</tr>
</table>
The framework of this project is shown below:
- ```UnrealCV``` is the basic bridge between ```Unreal Engine``` and ```OpenAI Gym```.
- ```OpenAI Gym``` is a toolkit for developing an RL algorithm, compatible with most numerical computation libraries, such as TensorFlow or PyTorch.
# Installation
## Dependencies
- UnrealCV
- Gym
- CV2
- Matplotlib
- Numpy
- Docker(Optional)
- Nvidia-Docker(Optional)
We recommend you use [anaconda](https://www.continuum.io/downloads) to install and manage your Python environment.
```CV2``` is used for image processing, like extracting object masks and bounding boxes. ```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](https://github.com/openai/gym), unrealcv, numpy and matplotlib are installed.
`Opencv` should be installed additionally.
If you use ```anaconda```, you can run
```
conda update conda
conda install --channel menpo opencv
```
or
```
pip install opencv-python
```
## Prepare Unreal Binary
Before running the environments, you need to prepare unreal binaries.
You can load them from clouds by running [load_env.py](load_env.py)
```
python load_env.py -e {ENV_NAME}
```
`ENV_NAME` can be `RealisticRoom`, `RandomRoom`, `Arm`, etc.
After that, it will automatically download a related env binary
to the [UnrealEnv](gym_unrealcv/envs/UnrealEnv) directory.
**Please refer the ``binary_list`` in [load_env.py](load_env.py) for more available example environments.**
# Usage
## 1. 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 UnrealSearch-RealisticRoomDoor-DiscreteColor-v0
```
After that, if all goes well， a pre-defined gym environment ```UnrealSearch-RealisticRoomDooExcerpt of 7,917 characters
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