mlsanityData to 14 Oct 2026

Context-Aware Model-Based Reinforcement Learning for Autonomous Racing

arXiv 2510.11501 ↗First posted 13 Oct 2025cs.LG cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Autonomous vehicles have shown promising potential to be a groundbreaking technology for improving the safety of road users. For these vehicles, as well as many other safety-critical robotic technologies, to be deployed in real-world applications, we require algorithms that can generalize well to unseen scenarios and data. Model-based reinforcement learning algorithms (MBRL) have demonstrated state-of-the-art performance and data efficiency across a diverse set of domains. However, these algorithms have also shown susceptibility to changes in the environment and its transition dynamics. In this work, we explore the performance and generalization capabilities of MBRL algorithms for autonomous driving, specifically in the simulated autonomous racing environment, Roboracer (formerly F1Tenth). We frame the head-to-head racing task as a learning problem using contextual Markov decision processes and parameterize the driving behavior of the adversaries using the context of the episode, thereby also parameterizing the transition and reward dynamics. We benchmark the behavior of MBRL algorithms in this environment and propose a novel context-aware extension of the existing literature, cMask. We demonstrate that context-aware MBRL algorithms generalize better to out-of-distribution adversary behaviors relative to context-free approaches. We also demonstrate that cMask displays strong generalization capabilities, as well as further performance improvement relative to other context-aware MBRL approaches when racing against adversaries with in-distribution behaviors.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 21 of its 33 references that are in the map. First posted on arXiv 13 Oct 2025.

Builds on

21 of 33 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo18 Jun

Other references · 20

  1. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,653
  2. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,264
  3. 2023Mastering Diverse Domains through World Modelscited by 1,253
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  5. 2020Mastering Atari with Discrete World Modelscited by 1,007
  6. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 170
  7. 2021Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observabilitycited by 109
  8. 2020Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learningcited by 65
  9. 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 64
  10. 2022Contextualize Me -- The Case for Context in Reinforcement Learningcited by 46
  11. 2018End-to-End Race Driving with Deep Reinforcement Learningcited by 40
  12. 2020Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloadscited by 30
8 more
  1. 2021Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racingcited by 26
  2. 2021Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Spacecited by 20
  3. 2024Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalizationcited by 16
  4. 2022Model- and Acceleration-based Pursuit Controller for High-Performance Autonomous Racingcited by 13
  5. 2023High-speed Autonomous Racing using Trajectory-aided Deep Reinforcement Learningcited by 10
  6. 2022TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approachcited by 8
  7. 2024Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarkscited by 8
  8. 2024RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learningcited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025On learning racing policies with reinforcement learning
  2. 2025Out-of-Distribution Generalization with a SPARC: Racing 100 Unseen Vehicles with a Single Policy
  3. 2022Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving
  4. 2025Action Space Reduction Strategies for Reinforcement Learning in Autonomous Driving
  5. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
  6. 2023FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
  7. 2024A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
  8. 2024Act Better by Timing: A timing-Aware Reinforcement Learning for Autonomous Driving