This is a game environment designed for Reinforcement Learning/Imitation Learning tasks. It is implemented using the 3D game engine Unity. We also implemented several algorithms based on this environment.

For people who wants to test their RL algorithms, you can download the build folder, and use without any further configurations. If you want to have more freedom in customizing the scene, for example, different routes, rewards settings, sensor numbers, please clone this repository and open it in Unity(2020.1.14f1). The Library folder is not included in this repository.

You can test the game by executing the RLCar.exe file in the build folder.

This game is built using Windows 10. Please use Windows(32/64 bit) if you want to directly run the executable. You can also build for other platforms using Unity.

We offer gym-like APIs in python 3. We recommend using Python 3.6 or above to interact with this environment.

Please install these dependencies before connecting.

pip install gym-unity==0.22.0 mlagents-envs==0.22.0

Include these libraries in Python.

from mlagents_envs.environment import UnityEnvironment
from gym_unity.envs import UnityToGymWrapper

You can connect the Unity environment using the binary executable file:

unity_env = UnityEnvironment("build\RLCar.exe")
env = UnityToGymWrapper(unity_env,0)

Or run it in the Unity Engine play mode:

unity_env = UnityEnvironment(base_port=5004)
env = UnityToGymWrapper(unity_env,0)

The APIs are similar to the gym.

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

Please check for the projects which uses this game environment.

We used DDPG to train the agent so it learns to drive using the horizontal and the vertical axes. The horizontal axis controls the steering while the vertical axis controls the acceleration.

The implementation can be found here.

We used DQN to train the agent after discretizing the action space.

The implementation can be found here.

We play the game and used the user-generated data to train the agent.

The implementation can be found here and here.

Songlin Hou, Ziyuan Chen, Akshata Pore and Soumya Balijepally