훈련 및 검증

강화 학습 에이전트 훈련 및 시뮬레이션하기

강화 학습 에이전트는 최적의 정책을 학습하기 위해 반복적인 시행착오 과정을 거치면서 환경과 상호 작용합니다. 에이전트는 훈련이 진행되는 동안 정책 표현의 파라미터를 조정하여 장기 보상을 최대화합니다. Reinforcement Learning Toolbox™는 시뮬레이션을 통해 에이전트를 훈련하고 훈련 결과를 검증하는 함수를 제공합니다. 자세한 내용은 강화 학습 에이전트 훈련시키기 항목을 참조하십시오.

앱

강화 학습 디자이너

강화 학습 에이전트 설계, 훈련 및 시뮬레이션 (R2021a 이후)

함수

모두 확장

에이전트 훈련시키기

`train`	Train reinforcement learning agents within a specified environment (R2019a 이후)
`rlTrainingOptions`	Options for training reinforcement learning agents (R2019a 이후)
`rlMultiAgentTrainingOptions`	Options for training multiple reinforcement learning agents (R2022a 이후)
`trainFromData`	Train off-policy reinforcement learning agent using existing data (R2023a 이후)
`rlTrainingFromDataOptions`	Options to train reinforcement learning agents using existing data (R2023a 이후)
`inspectTrainingResult`	Plot training information from a previous training session (R2021a 이후)

데이터 기록하기

`rlDataLogger`	Create either a file logger object or a monitor logger object to log training data (R2022b 이후)
`rlDataViewer`	Open Reinforcement Learning Data Viewer tool (R2023a 이후)
`FileLogger`	Log reinforcement learning training data to MAT-files (R2022b 이후)
`MonitorLogger`	Log reinforcement learning training data to monitor window (R2022b 이후)
`trainingProgressMonitor`	Monitor and plot training progress for deep learning custom training loops (R2022b 이후)
`setup`	Set up reinforcement learning environment or initialize data logger object (R2022a 이후)
`store`	Store data in the internal memory of a (file or monitor) logger object (R2022b 이후)
`write`	Transfer stored data from the internal logger memory to the logging target (R2022b 이후)
`cleanup`	Clean up reinforcement learning environment or data logger object (R2022a 이후)

에이전트 시뮬레이션하기

`sim`	Simulate trained reinforcement learning agents within specified environment (R2019a 이후)
`rlSimulationOptions`	Options for simulating a reinforcement learning agent within an environment (R2019a 이후)

사용자 지정 훈련

`runEpisode`	Simulate reinforcement learning environment against policy or agent (R2022a 이후)
`setup`	Set up reinforcement learning environment or initialize data logger object (R2022a 이후)
`cleanup`	Clean up reinforcement learning environment or data logger object (R2022a 이후)
`Future`	Object that supports deferred outputs for reinforcement learning environment simulations running on workers (R2022a 이후)
`fetchNext`	Retrieve next available unread outputs from a reinforcement learning environment simulations running on workers (R2022a 이후)
`fetchOutputs`	Retrieve results from all reinforcement learning environment simulations running on workers (R2022a 이후)
`cancel`	Cancel unfinished reinforcement learning environment simulations on workers (R2022a 이후)
`wait`	Wait for reinforcement learning environment simulations running on a workers to finish (R2022a 이후)

블록

RL Agent	강화 학습 에이전트 (R2019a 이후)
Policy	Reinforcement learning policy (R2022b 이후)

도움말 항목

훈련 및 시뮬레이션 기본 사항

강화 학습 에이전트 훈련시키기
지정된 환경 내에서 에이전트를 훈련시켜 최적의 정책을 찾습니다.
기본 그리드 월드에서 강화 학습 에이전트 훈련시키기
MATLAB^®에서 그리드 월드를 풀도록 Q-러닝 및 SARSA 에이전트를 훈련시킵니다.
MDP 환경에서 강화 학습 에이전트 훈련시키기
일반 마르코프 결정 과정 환경에서 강화 학습 에이전트를 훈련시킵니다.
Simulink 환경 만들기 및 에이전트 훈련시키기
Simulink^®에서 훈련 환경으로 모델링된 플랜트에 강화 학습을 사용하여 제어기를 훈련시킵니다.
Train Reinforcement Learning Agent for Simple Contextual Bandit Problem
Train Q and DQN agents to solve a contextual bandit problem.
Log Training Data to Disk
Log a variety of data to disk while training an agent.
Train Agent or Tune Environment Parameters Using Parameter Sweeping
Tune a DDPG agent using hyperparameter sweeping.

강화 학습 디자이너 앱 사용하기

Design and Train Agent Using Reinforcement Learning Designer
Design and train a DQN agent for a cart-pole system using the Reinforcement Learning Designer app.
Specify Simulation Options in Reinforcement Learning Designer
Interactively specify options for simulating reinforcement learning agents using the Reinforcement Learning Designer app.
Specify Training Options in Reinforcement Learning Designer
Interactively specify options for training reinforcement learning agents using the Reinforcement Learning Designer app.

다중 프로세스 및 GPU 사용하기

Train Agents Using Parallel Computing and GPUs
Accelerate agent training by running simulations in parallel on multiple cores, GPUs, clusters or cloud resources.
Train AC Agent to Balance Cart-Pole System Using Parallel Computing
Train a AC agent for a discrete action space environment using asynchronous parallel computing.
Train DQN Agent for Lane Keeping Assist Using Parallel Computing
Train a DQN agent for an automated driving application using parallel computing.

다중 에이전트 훈련

여러 개의 에이전트가 협업해서 작업을 수행하도록 훈련시키기
두 개의 연속 행동 공간 PPO 에이전트가 협업하여 객체를 옮기도록 훈련시킵니다.
Train Multiple Agents for Area Coverage
Train three discrete action space PPO agents to explore a grid-world environment in a collaborative-competitive manner.
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.

이중 적분기 시스템을 제어하도록 에이전트 훈련시키기

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.

카트-폴 시스템의 균형을 유지하도록 에이전트 훈련시키기

카트-폴 시스템의 균형을 유지하도록 DQN 에이전트 훈련시키기
MATLAB에서 모델링된 카트-폴 시스템의 균형을 유지하도록 DQN 에이전트를 훈련시킵니다.
Train PG Agent to Balance Cart-Pole System
Train a PG agent to balance a discrete action space cart-pole system modeled in MATLAB.
Train AC Agent to Balance Cart-Pole System
Train a AC agent to balance a discrete action space cart-pole system modeled in MATLAB.
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 에이전트 훈련시키기

진자가 위쪽으로 똑바로 서서 균형을 유지하도록 DQN 에이전트 훈련시키기
Simulink에서 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™.

제어 작업을 수행하도록 에이전트 훈련시키기

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.

로봇을 제어하도록 에이전트 훈련시키기

비행 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.

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.
적응형 크루즈 컨트롤을 위해 DDPG 에이전트 훈련시키기
적응형 크루즈 컨트롤 응용 사례를 위해 DDPG 에이전트를 훈련시킵니다.
Train DDPG Agent for Path-Following Control
Train a DDPG agent for a lane following application.
Train PPO Agent for Automatic Parking Valet
Train a discrete action space PPO agent to park a car in an open parking space.

기타 응용 사례

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.
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.

사용자 지정 에이전트 및 훈련 알고리즘 개발하기

Train Reinforcement Learning Policy Using Custom Training Loop
Train a reinforcement learning policy using your own custom training loop.
Custom Training Loop with Simulink Action Noise
Use a custom training loop to train a continuous action space reinforcement learning policy in Simulink when action noise is generated within the model.
Create and Train Custom PG Agent
Create a custom PG agent and train it using the built-in train function.
Create and Train Custom LQR Agent
Create a custom agent that solves an LQR problem and train it using the built-in train function.
Model-Based Reinforcement Learning Using Custom Training Loop
Create a model-based reinforcement learning agent using a custom training loop.

에이전트 및 정책 배포하기

Run SIL and PIL Verification for Reinforcement Learning
Verify a reinforcement learning agent in software-in-the-loop and processor-in-the-loop modes.
Generate Policy Block for Deployment
Generate a policy block to deploy a trained policy.