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
- 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. - 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. - 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. - 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. - 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. - 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.