mlsanityData to 7 Oct 2026

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

arXiv 2010.09776 ↗First posted 19 Oct 2020cs.MA cs.AI cs.GTcited by 121

Not on the map. Why?

Cited by 31 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently interact with diverse road users in diverse scenarios remains largely unsolved. Learning methods have much to offer towards solving this problem. But they require a realistic multi-agent simulator that generates diverse and competent driving interactions. To meet this need, we develop a dedicated simulation platform called SMARTS (Scalable Multi-Agent RL Training School). SMARTS supports the training, accumulation, and use of diverse behavior models of road users. These are in turn used to create increasingly more realistic and diverse interactions that enable deeper and broader research on multi-agent interaction. In this paper, we describe the design goals of SMARTS, explain its basic architecture and its key features, and illustrate its use through concrete multi-agent experiments on interactive scenarios. We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving. Our code is available at https://github.com/huawei-noah/SMARTS.

Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 25; 2022: 25; 2023: 21; 2024: 19; 2025: 17; 2026: 14.

121 papers.

Sort:

By area:

  1. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 281
  2. 2023DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Drivingcited by 233
  3. 2023Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Researchcited by 158
  4. 2021Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learningcited by 68
  5. 2022SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehiclescited by 65
  6. 20253D and 4D World Modeling: A Surveycited by 64
  7. 2021Stepwise Goal-Driven Networks for Trajectory Predictioncited by 62
  8. 2022Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulationcited by 61
  9. 2022Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real worldcited by 56
  10. 2023TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Predictioncited by 56
  11. 2024Learning Multi-Agent Communication from Graph Modeling Perspectivecited by 47
  12. 2026A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agentscited by 47
38 more
  1. 2021Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Drivingcited by 46
  2. 2021Settling the Variance of Multi-Agent Policy Gradientscited by 45
  3. 2022Hierarchical Model-Based Imitation Learning for Planning in Autonomous Drivingcited by 43
  4. 2021Modelling Behavioural Diversity for Learning in Open-Ended Gamescited by 39
  5. 2024RIME: Robust Preference-based Reinforcement Learning with Noisy Preferencescited by 37
  6. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 36
  7. 2021Reinforcement Learning for Ridesharing: An Extended Surveycited by 35
  8. 2021Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimizationcited by 33
  9. 2021Multi-Agent Constrained Policy Optimisationcited by 32
  10. 2021Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Taskscited by 32
  11. 2023Multi-agent Reinforcement Learning: A Comprehensive Surveycited by 30
  12. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 30
  13. 2023Rethinking Closed-loop Training for Autonomous Drivingcited by 29
  14. 2024DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experiencescited by 27
  15. 2024Improving Agent Behaviors with RL Fine-tuning for Autonomous Drivingcited by 27
  16. 2025Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environmentscited by 27
  17. 2023Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Drivingcited by 26
  18. 2021Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systemscited by 24
  19. 2022Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Drivingcited by 24
  20. 2024Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertaintycited by 24
  21. 2021DriverGym: Democratising Reinforcement Learning for Autonomous Drivingcited by 23
  22. 2022TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Drivingcited by 23
  23. 2024Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedbackcited by 20
  24. 2024POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfindingcited by 20
  25. 2023Large Sequence Models for Sequential Decision-Making: A Surveycited by 19
  26. 2022Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospectscited by 17
  27. 2022An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobilitycited by 16
  28. 2022Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Realcited by 16
  29. 2023Visual Imitation Learning with Patch Rewardscited by 16
  30. 2023Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influencecited by 16
  31. 2022ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependencycited by 15
  32. 2025Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Modelscited by 15
  33. 2022Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computationcited by 14
  34. 2023Rethinking Decision Transformer via Hierarchical Reinforcement Learningcited by 14
  35. 2021DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networkscited by 13
  36. 2024FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learningcited by 13
  37. 2021HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messagingcited by 12
  38. 2022Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimizationcited by 12

50 most cited of 121 All 121 (JSON)