MADRaS : Multi Agent Driving Simulator
Anirban Santara, Sohan Rudra, Sree Aditya Buridi, Meha Kaushik,, Abhishek Naik, Bharat Kaul, Balaraman Ravindran

TL;DR
MADRaS is an open-source multi-agent driving simulator built on TORCS, designed for developing and evaluating motion planning algorithms in complex, customizable traffic scenarios with reinforcement learning support.
Contribution
MADRaS introduces multi-agent training, inter-vehicular communication, and customizable traffic behaviors to TORCS, enabling advanced research in autonomous driving and curriculum learning.
Findings
Supports complex, tunable driving scenarios
Integrates with reinforcement learning frameworks
Enables multi-agent and communication research
Abstract
In this work, we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. MADRaS provides a platform for constructing a wide variety of highway and track driving scenarios where multiple driving agents can train for motion planning tasks using reinforcement learning and other machine learning algorithms. MADRaS is built on TORCS, an open-source car-racing simulator. TORCS offers a variety of cars with different dynamic properties and driving tracks with different geometries and surface properties. MADRaS inherits these functionalities from TORCS and introduces support for multi-agent training, inter-vehicular communication, noisy observations, stochastic actions, and custom traffic cars whose behaviours can be programmed to simulate challenging traffic conditions encountered in the real world.…
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Taxonomy
TopicsTraffic control and management · Autonomous Vehicle Technology and Safety · Transportation and Mobility Innovations
