mlsanityData to 14 Oct 2026

Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers

arXiv 2306.11466 ↗First posted 20 Jun 2023cs.RO cs.AI cs.LGcited by 2

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

Abstract (arXiv)

Developing and testing automated driving models in the real world might be challenging and even dangerous, while simulation can help with this, especially for challenging maneuvers. Deep reinforcement learning (DRL) has the potential to tackle complex decision-making and controlling tasks through learning and interacting with the environment, thus it is suitable for developing automated driving while not being explored in detail yet. This study carried out a comprehensive study by implementing, evaluating, and comparing the two DRL algorithms, Deep Q-networks (DQN) and Trust Region Policy Optimization (TRPO), for training automated driving on the highway-env simulation platform. Effective and customized reward functions were developed and the implemented algorithms were evaluated in terms of onlane accuracy (how well the car drives on the road within the lane), efficiency (how fast the car drives), safety (how likely the car is to crash into obstacles), and comfort (how much the car makes jerks, e.g., suddenly accelerates or brakes). Results show that the TRPO-based models with modified reward functions delivered the best performance in most cases. Furthermore, to train a uniform driving model that can tackle various driving maneuvers besides the specific ones, this study expanded the highway-env and developed an extra customized training environment, namely, ComplexRoads, integrating various driving maneuvers and multiple road scenarios together. Models trained on the designed ComplexRoads environment can adapt well to other driving maneuvers with promising overall performance. Lastly, several functionalities were added to the highway-env to implement this work. The codes are open on GitHub at https://github.com/alaineman/drlcarsim-paper.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 12 of its 23 references that are in the map. First posted on arXiv 20 Jun 2023.

Builds on

12 of 23 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning6 Jan

Other references · 11

  1. 2015Trust Region Policy Optimizationcited by 2,580
  2. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  3. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 225
  4. 2019A Theoretical Analysis of Deep Q-Learningcited by 215
  5. 2019Self-Driving Cars: A Surveycited by 148
  6. 2019A Survey of Deep Reinforcement Learning in Video Gamescited by 73
  7. 2018A Reinforcement Learning Based Approach for Automated Lane Change Maneuverscited by 46
  8. 2016End-to-End Deep Reinforcement Learning for Lane Keeping Assistcited by 27
  9. 2019Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environmentcited by 21
  10. 2017Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Mergecited by 19
  11. 2022Autonomous Highway Merging in Mixed Traffic Using Reinforcement Learning and Motion Predictive Safety Controllercited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networkscited by 5
  2. 2026Latent World Models for Automated Driving: A Unified Taxonomy, Evaluation Framework, and Open Challengescited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2023Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning
  2. 2025Deep reinforcement learning-based longitudinal control strategy for automated vehicles at signalised intersections
  3. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  4. 2024Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning
  5. 2023Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation
  6. 2026Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic
  7. 2022Prediction Based Decision Making for Autonomous Highway Driving
  8. 2026Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving