응용 사례

강화 학습을 적용하는 방법에 대한 예제

강화 학습은 제어, 로보틱스, 스케줄링, 최적화, 금융 등 서로 다른 분야의 다양한 문제에 적용할 수 있습니다. 다음은 몇 가지 예제입니다.

튜토리얼

카트-폴 시스템의 균형을 유지하도록 DQN 에이전트 훈련시키기
MATLAB^®에서 모델링된 카트-폴 시스템의 균형을 유지하도록 DQN 에이전트를 훈련시킵니다.
카트-폴 시스템의 균형을 유지하도록 PG 에이전트 훈련시키기
MATLAB에서 모델링된 이산 행동 공간 카트-폴 시스템의 균형을 유지하도록 PG 에이전트를 훈련시킵니다.
카트-폴 시스템의 균형을 유지하도록 AC 에이전트 훈련시키기
MATLAB에서 모델링된 이산 행동 공간 카트-폴 시스템의 균형을 유지하도록 AC 에이전트를 훈련시킵니다.
카트-폴 시스템이 위쪽으로 똑바로 서서 균형을 유지하도록 DDPG 에이전트 훈련시키기
Simscape™ Multibody™에서 모델링된 카트-폴 시스템이 위쪽으로 똑바로 서서 균형을 유지하도록 DDPG 에이전트를 훈련시킵니다.
Train MBPO Agent to Balance Cart-Pole System
A model-based reinforcement learning agent learns a model of its environment that it can use to generate additional experiences for training.

진자가 위쪽으로 똑바로 서서 균형을 유지하도록 DQN 에이전트 훈련시키기
Simulink^®에서 모델링된 진자가 위쪽으로 똑바로 서서 균형을 유지하도록 DQN 에이전트를 훈련시킵니다.
진자가 위쪽으로 똑바로 서서 균형을 유지하도록 DDPG 에이전트 훈련시키기
Simulink에서 모델링된 진자의 균형을 유지하도록 DDPG 에이전트를 훈련시킵니다.
Train DDPG Agent to Swing Up and Balance Pendulum with Bus Signal
Train a DDPG agent to balance a pendulum Simulink model that contains observations in a bus signal.
Train DDPG Agent to Swing Up and Balance Pendulum with Image Observation
Train a DDPG agent using an image-based observation signal.
Create DQN 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™.

Compare DDPG Agent to LQR Controller
Train a DDPG agent to control a second-order dynamic system modeled in MATLAB and compare it to an LQR controller.
Train PG Agent with Baseline to Control Discrete Action Space System
Train a PG agent with a baseline to control a discrete action space double integrator system modeled in MATLAB.

강화 학습을 사용하여 PI 제어기 조정하기
TD3 에이전트를 사용하여 PI 제어기의 이득을 조정합니다.
Train SAC Agent for Ball Balance Control
Train a SAC agent to balance a ball on a flat surface using a robot arm.
Train Reinforcement Learning Agents to Control Quanser QUBE Pendulum
Train SAC and PPO agents to balance the Quanser QUBE rotational inverted pendulum.
Train TD3 Agent for PMSM Control
Train a TD3 agent to control the currents in a permanent magnet synchronous motor.
Train DQN Agent with LSTM Network to Control House Heating System
Train a DQN agent with a recurrent network to control the temperature of an house.
Train Reinforcement Learning Agent with Constraint Enforcement
Train a DDPG agent with actions constrained using the Constraint Enforcement block.
Create and Train Custom LQR Agent
Create a custom agent that solves an LQR problem and train it using the built-in train function.

슬라이딩 로봇을 조종하도록 DDPG 에이전트 훈련시키기
비행 로봇 모델을 조종하도록 DDPG 에이전트를 훈련시킵니다.
Train PPO Agent for a Lander Vehicle
Train a PPO agent to land a discrete action space flying robot.
Train Biped Robot to Walk Using Reinforcement Learning Agents
Compare DDPG and TD3 agent for the control a biped walking robot modeled in Simscape Multibody.

Generate Reward Function from a Model Predictive Controller for a Servomotor
Generate a reward function from an MPC controller applied to a servomotor and use it to train a TD3 agent.
Generate Reward Function from a Model Verification Block for a Water Tank System
Generate a reward function from an model verification block applied to a water tank system and use it to train a TD3 agent.

Imitate MPC Controller for Lane Keeping Assist
Train a deep neural network to imitate the behavior of a model predictive controller within a lane keeping assist system.
Imitate Nonlinear MPC Controller for Flying Robot
Train a deep neural network to imitate the behavior of a nonlinear model predictive controller for a flying robot.
Train DDPG Agent with Pretrained Actor Network
Train a DDPG agent using an actor network that has been previously trained using supervised learning.

차선 유지 보조를 위해 DQN 에이전트 훈련시키기
차선 유지 보조 응용 사례를 위해 DQN 에이전트를 훈련시킵니다.
적응형 크루즈 컨트롤을 위해 DDPG 에이전트 훈련시키기
적응형 크루즈 컨트롤 응용 사례를 위해 DDPG 에이전트를 훈련시킵니다.
경로 추종 컨트롤을 위해 DDPG 에이전트 훈련시키기
차선 추종 응용 사례를 위해 DDPG 에이전트를 훈련시킵니다.
Train PPO Agent for Automatic Parking Valet
Train a discrete action space PPO agent to park a car in an open parking space.

Deep Reinforcement Learning for Optimal Trade Execution
This example shows how to use the Reinforcement Learning Toolbox™ and Deep Learning Toolbox™ to design agents for optimal trade execution.
Multiperiod Goal-Based Wealth Management Using Reinforcement Learning
This example shows a reinforcement learning (RL) approach to maximize the probability of obtaining an investor's wealth goal at the end of the investment horizon.
Train DQN Agent for Beam Selection
Train a deep Q-network (DQN) reinforcement learning agent for beam selection in a 5G new radio communications system.
Water Distribution System Scheduling Using Reinforcement Learning
Train a DQN agent to optimally activate pumps in a water distribution system.

Train MBPO Agent to Balance Cart-Pole System
A model-based reinforcement learning agent learns a model of its environment that it can use to generate additional experiences for training.
Model-Based Reinforcement Learning Using Custom Training Loop
Create a model-based reinforcement learning agent using a custom training loop.