mlsanityData to 14 Oct 2026

Adversarial Agent Behavior Learning in Autonomous Driving Using Deep Reinforcement Learning

arXiv 2508.15207 ↗First posted 21 Aug 2025cs.CVcited by 0

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

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Abstract (arXiv)

Existing approaches in reinforcement learning train an agent to learn desired optimal behavior in an environment with rule based surrounding agents. In safety critical applications such as autonomous driving it is crucial that the rule based agents are modelled properly. Several behavior modelling strategies and IDM models are used currently to model the surrounding agents. We present a learning based method to derive the adversarial behavior for the rule based agents to cause failure scenarios. We evaluate our adversarial agent against all the rule based agents and show the decrease in cumulative reward.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 14 of its 26 references that are in the map. First posted on arXiv 21 Aug 2025.

Builds on

14 of 26 references.

References · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Intriguing properties of neural networkscited by 5,155
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2015Trust Region Policy Optimizationcited by 2,580
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  6. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  7. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  8. 2017Robust Adversarial Reinforcement Learningcited by 451
  9. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 311
  10. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 225
  11. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Drivingcited by 98
  12. 2017Control of a Quadrotor with Reinforcement Learningcited by 92
2 more
  1. 2019Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learningcited by 53
  2. 2017Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Mergecited by 19

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  1. 2021Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies
  2. 2022Security of Deep Reinforcement Learning for Autonomous Driving: A Survey
  3. 2025Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
  4. 2021Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment
  5. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning
  6. 2023Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation
  7. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey
  8. 2023Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving