mlsanityData to 7 Oct 2026

Randomized Adversarial Imitation Learning for Autonomous Driving

arXiv 1905.05637 ↗First posted 13 May 2019cs.RO cs.AI cs.LGcited by 6

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

With the evolution of various advanced driver assistance system (ADAS) platforms, the design of autonomous driving system is becoming more complex and safety-critical. The autonomous driving system simultaneously activates multiple ADAS functions; and thus it is essential to coordinate various ADAS functions. This paper proposes a randomized adversarial imitation learning (RAIL) method that imitates the coordination of autonomous vehicle equipped with advanced sensors. The RAIL policies are trained through derivative-free optimization for the decision maker that coordinates the proper ADAS functions, e.g., smart cruise control and lane keeping system. Especially, the proposed method is also able to deal with the LIDAR data and makes decisions in complex multi-lane highways and multi-agent environments.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 17 of its 31 references that are in the map. First posted on arXiv 13 May 2019.

Builds on

17 of 31 references.

References · 17

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2016Least Squares Generative Adversarial Networkscited by 1,000
  5. 2017Deep Reinforcement Learning that Matterscited by 727
  6. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 555
  7. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  8. 2017Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuningcited by 288
  9. 2018Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learningcited by 203
  10. 2016A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Modelscited by 167
  11. 2017Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Controlcited by 92
  12. 2018Simple random search provides a competitive approach to reinforcement learningcited by 75
5 more
  1. 2017Towards Generalization and Simplicity in Continuous Controlcited by 65
  2. 2017OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learningcited by 40
  3. 2018Automated Speed and Lane Change Decision Making using Deep Reinforcement Learningcited by 36
  4. 2016End-to-End Deep Reinforcement Learning for Lane Keeping Assistcited by 27
  5. 2018Generative Adversarial Self-Imitation Learningcited by 25

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 1.

6 papers.

  1. 2021Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Gamescited by 3
  2. 2021Adversarial Imitation Learning via Random Search in Lane Change Decision-Makingcited by 2
  3. 2021Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehiclescited by 1
  4. 2022Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networkscited by 0
  5. 2022Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perceptioncited by 0
  6. 2024RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments
  2. 2019Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety
  3. 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
  4. 2021Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies
  5. 2022Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
  6. 2024Hybrid Imitation-Learning Motion Planner for Urban Driving
  7. 2021Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving
  8. 2019Learning a Decision Module by Imitating Driver's Control Behaviors