mlsanityData to 7 Oct 2026

Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions

arXiv 2103.07903 ↗First posted 14 Mar 2021cs.AIcited by 3

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

Abstract (arXiv)

Applications of reinforcement learning (RL) are popular in autonomous driving tasks. That being said, tuning the performance of an RL agent and guaranteeing the generalization performance across variety of different driving scenarios is still largely an open problem. In particular, getting good performance on complex road and weather conditions require exhaustive tuning and computation time. Curriculum RL, which focuses on solving simpler automation tasks in order to transfer knowledge to complex tasks, is attracting attention in RL community. The main contribution of this paper is a systematic study for investigating the value of curriculum reinforcement learning in autonomous driving applications. For this purpose, we setup several different driving scenarios in a realistic driving simulator, with varying road complexity and weather conditions. Next, we train and evaluate performance of RL agents on different sequences of task combinations and curricula. Results show that curriculum RL can yield significant gains in complex driving tasks, both in terms of driving performance and sample complexity. Results also demonstrate that different curricula might enable different benefits, which hints future research directions for automated curriculum training.

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 10 of its 26 references that are in the map. First posted on arXiv 14 Mar 2021.

Builds on

10 of 26 references.

References · 10

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 350
  3. 2017Automated Curriculum Learning for Neural Networkscited by 251
  4. 2017Teacher-Student Curriculum Learningcited by 197
  5. 2018Learning Curriculum Policies for Reinforcement Learningcited by 34
  6. 2018Mix&Match - Agent Curricula for Reinforcement Learningcited by 26
  7. 2019Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environmentcited by 20
  8. 2018Accuracy-based Curriculum Learning in Deep Reinforcement Learningcited by 15
  9. 2020Automatic Curriculum Learning With Over-repetition Penalty for Dialogue Policy Learningcited by 8
  10. 2020Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agentscited by 3

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.

3 papers.

  1. 2025CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Modelscited by 11
  2. 2023Continual Driving Policy Optimization with Closed-Loop Individualized Curriculacited by 4
  3. 2022DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Drivingcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning
  2. 2025Diverse and Adaptive Behavior Curriculum for Autonomous Driving: A Student-Teacher Framework with Multi-Agent RL
  3. 2026Scaling Curriculum Learning For Autonomous Driving
  4. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
  5. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
  6. 2022Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills
  7. 2025LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models
  8. 2021A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios