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심층강화학습기반 장애물과 신호등을 고려한 다차선 자율주행 연구

Roff 0.05% Python 37.04% ASP.NET 62.91%
deep-q-network deep-reinforcement-learning ml-agents prioritized-experience-replay python unity virtual-environment epsilon-greedy-exploration noisy-networks

multi-lane-autonomous-driving-based-on-deep-reinforcement-learning-considering-obs-trafficsig's Introduction

장애물과 신호등을 고려한 디지털 가상환경 모델 구현 및 심층강화학습기반 다차선 자율주행 연구

  • Unity + ML_Agent toolkit

    1. 도로 주행 환경
    2. 차량 에이전트 구현
    3. 가상 도로 구현 (Unity Asset 활용)
    4. 차량 장애물 및 장애물 오브젝트 배치
  • Python API

    1. 심층 강화학습 모델 (customed DQN)
    2. Pytorch + CuPy/NumPy + CUDA (Anaconda 사용)
    3. Customed DQN(Deep Q-Network) = Vanilla DQN + Dueling Architecture + PER(Prioritized Experience Replay) + NoisyNet
    4. Epsilon Greedy decaying, Soft target Update

    *추가로 고려해본 기법: Multi-step Learning, Distributional Q-learning

결과분석: Proposed System vs Vanilla DQN vs NoisyNet

  • 학습의 수렴성 및 안정성 증가
  • 학습 효율성 개선
  • 차선 유지, 장애물 회피, 신호 준수 자율 주행 성공

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