응용 사례

튜토리얼

제어 작업을 수행하도록 에이전트 훈련시키기

• DDPG 에이전트를 사용하여 탱크의 수위 제어하기
  Simulink^®에서 모델링된 플랜트를 훈련 환경으로 설정하여 강화 학습을 사용해 제어기를 훈련시킵니다.
• 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.
• DDPG 에이전트를 사용하여 탱크의 수위 제어하기
  Simulink에서 모델링된 플랜트를 훈련 환경으로 설정하여 강화 학습을 사용해 제어기를 훈련시킵니다.
• Train Reinforcement Learning Agents to Control Quanser QUBE Pendulum
  Train SAC and PPO agents to balance the Quanser QUBE rotational inverted pendulum.
• Train Reinforcement Learning Agent Offline to Control Quanser QUBE Pendulum
  Train TD3 agent offline to control a Quanser QUBE pendulum.
• Train TD3 Agent for PMSM Control
  Train a TD3 agent to control the currents in a permanent magnet synchronous motor.
• Field-Oriented Control of PMSM Using Reinforcement Learning (Motor Control Blockset)
  This example shows you how to use the control design method of reinforcement learning to implement field-oriented control (FOC) of a permanent magnet synchronous motor (PMSM).
• 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 (Simulink Control Design)
  Train a reinforcement learning 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.

로봇을 제어하도록 에이전트 훈련시키기

• Train DDPG Agent to Control Sliding Robot
  Train a DDPG agent to control a robot sliding over a frictionless 2-D plane.
• Train PPO Agent for a Lander Vehicle
  Train a discrete PPO agent to land a flying vehicle.
• Train Discrete Soft Actor Critic Agent for Lander Vehicle
  Train a discrete SAC agent to land a flying vehicle.
• 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™.
• Train Biped Robot to Walk Using Evolution Strategy-Reinforcement Learning Agents
  Train TD3 agent using evolutionary strategy.
• DDPG 에이전트를 사용한 사족 보행 로봇 운동
  Simscape Multibody에서 모델링된 4족 보행 로봇을 제어하도록 DDPG 에이전트를 훈련시킵니다.

제어 사양에서 보상 생성하기

• 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.

모방 학습

• 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.

자동차 응용 사례를 위한 에이전트 훈련시키기

• 차선 유지 보조를 위해 DQN 에이전트 훈련시키기
  차선 유지 보조 응용 사례를 위해 DQN 에이전트를 훈련시킵니다.
• Train PPO Agent with Curriculum Learning for a Lane Keeping Application
  Train a PPO agent for a lane keeping assist task by gradually increasing task complexity.
• 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 Multiple Agents for Path Following Control
  Train a DQN and a DDPG agent to collaboratively perform adaptive cruise control and lane keeping assist to follow a path.
• Train Hybrid SAC Agent for Path-Following Control
  Train an hybrid SAC agent for lane following control.
• Train PPO Agent for Automatic Parking Valet
  Train a discrete action space PPO agent to park a car in an open parking space.
• Automatic Parking Valet with Unreal Engine Simulation
  Use a TD3 agent with an MPC controller to perform a parking maneuver.

기타 응용 사례

• Train Reinforcement Learning Agent for Simple Contextual Bandit Problem
  Train Q and DQN agents to solve a contextual bandit problem.
• Train Agent to Play Turn-Based Game
  Train a DQN agent to play a turn-based game.
• 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 (Communications Toolbox)
  Train a deep Q-network (DQN) reinforcement learning agent for beam selection in a 5G new radio communications system. (R2022b 이후)
• Water Distribution System Scheduling Using Reinforcement Learning
  Train a DQN agent to optimally activate pumps in a water distribution system.

모델 기반의 정책 최적화 에이전트 훈련시키기

• Train MBPO Agent to Balance Continuous 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.

추천 예제

Identify Vulnerabilities in DC Microgrids

Train a TD3 agent to attack a cyber-physical system to identify vulnerabilities.

• 라이브 스크립트 열기

Train Agent to Play Turn-Based Game

Train a DQN agent to play a turn-based game.

• 라이브 스크립트 열기

Automatic Parking Valet with Unreal Engine Simulation

Use a TD3 agent with an MPC controller to perform a parking maneuver.

• 라이브 스크립트 열기

DDPG 에이전트를 사용한 사족 보행 로봇 운동

Simscape Multibody에서 모델링된 4족 보행 로봇을 제어하도록 DDPG 에이전트를 훈련시킵니다.

• 라이브 스크립트 열기