Main Content

이 번역 페이지는 최신 내용을 담고 있지 않습니다. 최신 내용을 영문으로 보려면 여기를 클릭하십시오.

딥러닝을 사용한 강화 학습

강화 학습을 사용하여 심층 신경망 에이전트 훈련시키기

심층 신경망을 이용한 강화 학습에 적용할 정책과 가치 함수 표현식을 작성할 수 있습니다. 그런 다음 Reinforcement Learning Toolbox™를 사용하여 이러한 신경망을 훈련시킬 수 있습니다. 자세한 내용은 What Is Reinforcement Learning? (Reinforcement Learning Toolbox), Create Policy and Value Function Representations (Reinforcement Learning Toolbox) 항목과 Train Reinforcement Learning Agents (Reinforcement Learning Toolbox) 항목을 참조하십시오.

도움말 항목

Create Simulink Environment and Train Agent

Train a controller using reinforcement learning with a plant modeled in Simulink® as the training environment.

Create Agent Using Deep Network Designer and Train Using Image Observations

Create a reinforcement learning agent using the Deep Network Designer app from the Deep Learning Toolbox™.

Train DDPG Agent to Swing Up and Balance Pendulum with Image Observation

Train a reinforcement learning agent using an image-based observation signal.

Train DQN Agent for Lane Keeping Assist Using Parallel Computing

Train a reinforcement learning agent for a lane keeping assist application.

추천 예제

Train DDPG Agent to Control Flying Robot

Train DDPG Agent to Control Flying Robot

Train a reinforcement learning agent to control a flying robot model.

라이브 스크립트 열기
Train Biped Robot to Walk Using Reinforcement Learning Agents

Train Biped Robot to Walk Using Reinforcement Learning Agents

Train a reinforcement learning agent to control a biped walking robot modeled in Simscape™ Multibody™.

라이브 스크립트 열기
Train DDPG Agent for Adaptive Cruise Control

Train DDPG Agent for Adaptive Cruise Control

Train a reinforcement learning agent for an adaptive cruise control application.

라이브 스크립트 열기
Train DDPG Agent for Path-Following Control

Train DDPG Agent for Path-Following Control

Train a reinforcement learning agent for a lane following application.

라이브 스크립트 열기

Deep Learning Toolbox 문서

지원

Introducing Deep Learning with MATLAB

Introducing Deep Learning with MATLAB

Download ebook