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MACAD-Gym v0.1.5

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@release-drafter release-drafter released this 28 Nov 02:02
· 3 commits to refs/heads/master since this release
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MACAD-Gym learning environment 1
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