mlsanityData to 7 Oct 2026

Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning

arXiv 2104.08876 ↗First posted 18 Apr 2021cs.LG cs.SY eess.SYcited by 3

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endangering other road users - under a wide range of traffic cultures and driving conditions. While deep reinforcement learning methods have excelled in recent years and been applied to automated vehicle driving policy, there are concerns about their capability to quickly adapt to unseen traffic with new environment dynamics. We formulate this challenge as a multi-Markov Decision Processes (MDPs) adaptation problem and developed Meta Reinforcement Learning (MRL) driving policies to showcase their quick learning capability. Two types of distribution variation in environments were designed and simulated to validate the fast adaptation capability of resulting MRL driving policies which significantly outperform a baseline RL.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 22 of its 35 references that are in the map. First posted on arXiv 18 Apr 2021.

Builds on

22 of 35 references.

References · 22

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Trust Region Policy Optimizationcited by 2,503
  4. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
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  7. 2019Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variablescited by 372
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  10. 2019Challenges of Real-World Reinforcement Learningcited by 282
  11. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 278
  12. 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 270
10 more
  1. 2017One-Shot Visual Imitation Learning via Meta-Learningcited by 225
  2. 2018One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learningcited by 146
  3. 2018Meta-Reinforcement Learning of Structured Exploration Strategiescited by 128
  4. 2017On the State of the Art of Evaluation in Neural Language Modelscited by 103
  5. 2018ProMP: Proximal Meta-Policy Searchcited by 91
  6. 2015Memory-based control with recurrent neural networkscited by 77
  7. 2020Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimizationcited by 34
  8. 2017Deep Learning for Video Game Playingcited by 30
  9. 2019Autonomous Highway Driving using Deep Reinforcement Learningcited by 22
  10. 2020Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehiclescited by 3

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 2.

3 papers.

  1. 2023Testing of Deep Reinforcement Learning Agents with Surrogate Modelscited by 12
  2. 2023Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imaginationcited by 6
  3. 2021Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularizationcited by 4

Similar, not cited

Close in wording, no citation either way

  1. 2020Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles
  2. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
  3. 2026Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving
  4. 2023Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors
  5. 2025LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models
  6. 2024Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement
  7. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
  8. 2023Parameterized Decision-making with Multi-modal Perception for Autonomous Driving