# MADRaS : Multi Agent Driving Simulator

arXiv: https://arxiv.org/abs/2010.00993 · HTML: https://mlsanity.com/paper/2010.00993/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021)
- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world](https://mlsanity.com/paper/2206.09889/) (2022)
- [GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS](https://mlsanity.com/paper/2408.01584/) (2024)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion](https://mlsanity.com/paper/2308.06931/) (2023)
- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters](https://mlsanity.com/paper/2405.04491/) (2024)
- [Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review](https://mlsanity.com/paper/2409.14196/) (2024)
- [Getting SMARTER for Motion Planning in Autonomous Driving Systems](https://mlsanity.com/paper/2502.15824/) (2025)
- [Control-ITRA: Controlling the Behavior of a Driving Model](https://mlsanity.com/paper/2501.12408/) (2025)
- [MACRPO: Multi-Agent Cooperative Recurrent Policy Optimization](https://mlsanity.com/paper/2109.00882/) (2021)
- [Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning](https://mlsanity.com/paper/2310.08331/) (2023)
- [A New Approach to Training Multiple Cooperative Agents for Autonomous Driving](https://mlsanity.com/paper/2209.02157/) (2022)
- [Enhancing Deep Deterministic Policy Gradients on Continuous Control Tasks with Decoupled Prioritized Experience Replay](https://mlsanity.com/paper/2512.05320/) (2025)

## Abstract (arXiv)

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. MADRaS can be used to create driving tasks whose complexities can be tuned along eight axes in well-defined steps. This makes it particularly suited for curriculum and continual learning. MADRaS is lightweight and it provides a convenient OpenAI Gym interface for independent control of each car. Apart from the primitive steering-acceleration-brake control mode of TORCS, MADRaS offers a hierarchical track-position -- speed control that can potentially be used to achieve better generalization. MADRaS uses multiprocessing to run each agent as a parallel process for efficiency and integrates well with popular reinforcement learning libraries like RLLib.