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 Train Reinforcement Learning Agents 항목을 참조하십시오.

앱

강화 학습 디자이너

강화 학습 에이전트 설계, 훈련 및 시뮬레이션

함수

`train`	Train reinforcement learning agents within a specified environment
`rlTrainingOptions`	Options for training reinforcement learning agents
`sim`	Simulate trained reinforcement learning agents within specified environment
`rlSimulationOptions`	Options for simulating a reinforcement learning agent within an environment
`inspectTrainingResult`	Plot training information from a previous training session

블록

RL Agent

강화 학습 에이전트

도움말 항목

훈련 및 시뮬레이션 기본 사항

Train Reinforcement Learning Agents
Find the optimal policy by training your agent within a specified environment.
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.

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.
Specify Training Options in Reinforcement Learning Designer
Interactively specify options for training reinforcement learning agents.

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 actor-critic agent using asynchronous parallel computing.
Train DQN Agent for Lane Keeping Assist Using Parallel Computing
Train a reinforcement learning agent for an automated driving application using parallel computing.

MATLAB 환경에서 에이전트 훈련하기

Train DDPG Agent to Control Double Integrator System
Train a deep deterministic policy gradient agent to control a second-order dynamic system modeled in MATLAB.
Train PG Agent with Baseline to Control Double Integrator System
Train a policy gradient with a baseline to control a double integrator system modeled in MATLAB.
Train DQN Agent to Balance Cart-Pole System
Train a deep Q-learning network agent to balance a cart-pole system modeled in MATLAB.
Train PG Agent to Balance Cart-Pole System
Train a policy gradient agent to balance a cart-pole system modeled in MATLAB.
Train AC Agent to Balance Cart-Pole System
Train an actor-critic agent to balance a cart-pole system modeled in MATLAB.
Train DDPG Agent to Swing Up and Balance Pendulum with Image Observation
Train a reinforcement learning agent using an image-based observation signal.
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™.

Simulink 환경에서 에이전트 훈련하기

Train DQN Agent to Swing Up and Balance Pendulum
Train a Deep Q-network agent to balance a pendulum modeled in Simulink.
Train DDPG Agent to Swing Up and Balance Pendulum
Train a deep deterministic policy gradient agent to balance a pendulum modeled in Simulink.
Train DDPG Agent to Swing Up and Balance Pendulum with Bus Signal
Train a reinforcement learning agent to balance a pendulum Simulink model that contains observations in a bus signal.
Train DDPG Agent to Swing Up and Balance Cart-Pole System
Train a deep deterministic policy gradient agent to swing up and balance a cart-pole system modeled in Simscape™ Multibody™.

다중 에이전트 훈련

Train Multiple Agents to Perform Collaborative Task
Train two PPO agents to collaboratively move an object.
Train Multiple Agents for Area Coverage
Train three 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.

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.
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.

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 reinforcement learning agent using an actor network that has been previously trained using supervised learning.