Adversarial Agent Behavior Learning in Autonomous Driving Using Deep Reinforcement Learning

2025-08-21 · Arjun Srinivasan, Anubhav Paras, Aniket Bera 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.

📄 PDF Abstract BibTeX arXiv:2508.15207

Code (0)

등록된 구현이 없습니다.

Tasks

Reinforcement Learning Autonomous Driving

Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies

2021-12-22 · Aizaz Sharif, Dusica Marijan

Autonomous cars are well known for being vulnerable to adversarial attacks that can compromise the safety of the car and pose danger to other road users. To effectively defend against adversaries, it is required to not o…

Autonomous Driving Deep Reinforcement Learning Reinforcement Learning (RL)

Automatic Testing With Reusable Adversarial Agents

2019-10-30 · Xin Qin, Nikos Aréchiga, Andrew Best, Jyotirmoy Deshmukh

Autonomous systems such as self-driving cars and general-purpose robots are safety-critical systems that operate in highly uncertain and dynamic environments. We propose an interactive multi-agent framework where the sys…

Autonomous Vehicles Deep Reinforcement Learning Reinforcement Learning Self-Driving Cars

Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving

2019-03-26 · Akifumi Wachi

We examine the problem of adversarial reinforcement learning for multi-agent domains including a rule-based agent. Rule-based algorithms are required in safety-critical applications for them to work properly in a wide ra…

Autonomous Driving Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions

2025-05-18 · Albert Zhao, Stefano Soatto

We describe a robust planning method for autonomous driving that mixes normal and adversarial agent predictions output by a diffusion model trained for motion prediction. We first train a diffusion model to learn an unbi…

Autonomous Driving motion prediction

Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning

2019-11-19 · Pin Wang, Dapeng Liu, Jiayu Chen, Hanhan Li 외

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) …

Autonomous Driving Decision Making Imitation Learning reinforcement-learning+2