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심층강화학습기반 디지털 트윈 환경에서의 자율주행 연구

Python 56.79% C# 43.21%
autonomous-driving deep-q-network reinforcement-learning unity unity-ml-agents

autonomous-driving-research-based-deep-reinforcement-learning-considering-digital-twin's Introduction

Unity 디지털 트윈 환경에서의 자율주행 DQN 연구

구현 목표: 구축 환경에서 심층 강화학습으로 차션 유지, 장애물 회피 주행하며 빠른 속도로 한 바퀴 주행

데이터 통신

  • Unity + ML-Agent Toolkit 을 활용한 실세계와 유사한 가상 도로 환경 구축

    1. 왕복 4차선 도로 도로전면

    2. Box Collider를 이용한 도로 연석 구현 연석

    3. Ray Perception Sensor, Camera Sensor를 부착한 차량 에이전트 활용 차량에이전트

    4. Unity Asset : Stylized Vehicles Pack - FREE (에이전트, 장애물 차량), Cartoon Road Constructor (도로)

  • Python API를 활용한 DQN(Deep Q-Network) 구조 설계

    1. Using: PyTorch, CuPy, Numpy, CUDA, Anaconda
    2. Input(State Space): Image queue, Ray distance list
    3. Action space: 15개의 discrete actions

DQN 구조도 drawio

3000 episode 학습 결과 (4배속)

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