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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)

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 installed, 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.

See full README for more information.

Summary of updates in v0.1.4

• Update Pedestrian -> Walker in actor type
• Yaml -> yml
• Update version number in docs conf
• Add env.close() to properly cleanup sim server proc @praveen-palanisamy (#25)
• Improve code maintainability @praveen-palanisamy (#18)
• Added py pkg badges to README @praveen-palanisamy (#17)

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 installed, 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.

See full README for more information.

Summary of updates in v0.1.3

• Updated python package version @praveen-palanisamy (#16)
• Added github action for pub to PyPI on creation of a release
• Fixed release-drafter config: yaml value should be str @praveen-palanisamy (#12)
• Added no-response bot @praveen-palanisamy (#11)
• Added release-drafter @praveen-palanisamy (#10)
• Added example for a basic agent script @praveen-palanisamy (#9)
• Added fixed_delta_seconds when running in synchronous mode to allow for proper physics sub-stepping in sync @praveen-palanisamy (#8)
• Fixed typo and dict access in Agent interface example
• Updated README
• Added NeurIPS paper info to README

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 installed, 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.

See full README for more information.