Adversarial Agent Behavior Learning in Autonomous Driving Using Deep Reinforcement Learning

Fuente: arXiv
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Main Authors: Srinivasan, Arjun, Paras, Anubhav, Bera, Aniket
Format: Preprint
Published: 2025
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author Srinivasan, Arjun
Paras, Anubhav
Bera, Aniket
author_facet Srinivasan, Arjun
Paras, Anubhav
Bera, Aniket
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2508_15207
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Adversarial Agent Behavior Learning in Autonomous Driving Using Deep Reinforcement Learning
Srinivasan, Arjun
Paras, Anubhav
Bera, Aniket
Computer Vision and Pattern Recognition
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.
title Adversarial Agent Behavior Learning in Autonomous Driving Using Deep Reinforcement Learning
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2508.15207