mlsanityData to 7 Oct 2026

Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning

arXiv 1911.08044 ↗First posted 19 Nov 2019cs.AI cs.LGcited by 0

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

Abstract (arXiv)

Making decisions in complex driving environments is a challenging task for autonomous agents. Imitation learning methods have great potentials for achieving such a goal. Adversarial Inverse Reinforcement Learning (AIRL) is one of the state-of-art imitation learning methods that can learn both a behavioral policy and a reward function simultaneously, yet it is only demonstrated in simple and static environments where no interactions are introduced. In this paper, we improve and stabilize AIRL's performance by augmenting it with semantic rewards in the learning framework. Additionally, we adapt the augmented AIRL to a more practical and challenging decision-making task in a highly interactive environment in autonomous driving. The proposed method is compared with four baselines and evaluated by four performance metrics. Simulation results show that the augmented AIRL outperforms all the baseline methods, and its performance is comparable with that of the experts on all of the four metrics.

Key facts. No citing papers in the map yet (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 11 of its 23 references that are in the map. First posted on arXiv 19 Nov 2019.

Builds on

11 of 23 references.

References · 11

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  5. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  6. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 301
  7. 2016A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Modelscited by 167
  8. 2019End-to-End Robotic Reinforcement Learning without Reward Engineeringcited by 115
  9. 2017Imitating Driver Behavior with Generative Adversarial Networkscited by 92
  10. 2018A Reinforcement Learning Based Approach for Automated Lane Change Maneuverscited by 44
  11. 2018Automated Driving Maneuvers under Interactive Environment based on Deep Reinforcement Learningcited by 4

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2020Off-Policy Adversarial Inverse Reinforcement Learning
  2. 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learning
  3. 2024Deep Reinforcement Learning for Local Path Following of an Autonomous Formula SAE Vehicle
  4. 2021Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks
  5. 2025CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving
  6. 2020Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
  7. 2024Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation, Transferable Reward Recovery and Algebraic Equilibrium Proof
  8. 2019Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving