Main Content

이 페이지의 최신 내용은 아직 번역되지 않았습니다. 최신 내용은 영문으로 볼 수 있습니다.

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) 및 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