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. Quick Start 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. Training RL Agents 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. Visualizing the Environment 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. Summary of updates in v0.1.5 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)

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