MACAD-Gym, Multi-Agent Reinforcement Learning for Connected Autonomous Driving

MACAD-Gym is a training platform for Multi-Agent Connected Autonomous Driving (MACAD) built on top of the CARLA Autonomous Driving simulator.

MACAD-Gym provides OpenAI Gym-compatible learning environments for various driving scenarios for training Deep RL algorithms in homogeneous/heterogenous, communicating/non-communicating and other multi-agent settings. New environments and scenarios can be easily added using a simple, JSON-like configuration.

Install MACAD-Gym using pip install macad-gym. If you have CARLA_SERVER setup, you can get going using the following 3 lines of code. If not, follow the Getting started steps.

import gym
import macad_gym
env = gym.make("HomoNcomIndePOIntrxMASS3CTWN3-v0")

# Your agent code here

Any RL library that supports the OpenAI-Gym API can be used to train agents in MACAD-Gym. The MACAD-Agents repository provides sample agents as a starter.

To test-drive the environments, you can run the environment script directly. For example, to test-drive the HomoNcomIndePOIntrxMASS3CTWN3-v0 environment, run:

python -m macad_gym.envs.homo.ncom.inde.po.intrx.ma.stop_sign_3c_town03

See full README for more information.

• Update readme, add citation.cff @praveen-palanisamy (#75)
• Fix multi view render @praveen-palanisamy (#74)
• Npc traffic spawning feature @johnMinelli (#70)
• Add support for Windows platform and some bug fixes @Morphlng (#65)