用途

強化学習を適用する方法の例

強化学習は、制御、ロボティクス、スケジューリング、最適化、金融など、幅広い分野のさまざまな問題に適用できます。こちらはその例です。

チュートリアル

カートポールシステムの平衡化のための DQN エージェントの学習
MATLAB^® でモデル化されたカートポールシステムの平衡化を行うために DQN エージェントに学習させる。
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.
Train DDPG Agent to Swing Up and Balance Cart-Pole System
Train a DDPG agent to swing up and balance a cart-pole system modeled in Simscape™ Multibody™.
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.

振子の振り上げと平衡化のための DQN エージェントの学習
Simulink^® でモデル化された振子の振り上げと平衡化を行うように、DQN エージェントに学習させる。
振子の振り上げと平衡化のための DDPG エージェントの学習
Simulink でモデル化された振子の平衡化を行うために DDPG エージェントに学習させる。
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.
ディープネットワークデザイナーを使用した DQN エージェントの作成およびイメージ観測値を使用した学習
Deep Learning Toolbox™ のディープネットワークデザイナーアプリを使用して、強化学習エージェントを作成する。

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.

強化学習を使用した PI コントローラーの調整
TD3 エージェントを使用して PI コントローラーのゲインを調整する。
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.

Train DDPG Agent to Control Sliding Robot
Train a DDPG agent to control a flying robot model.
Train PPO Agent for a Lander Vehicle
Train a PPO agent to land a discrete action space flying robot.
強化学習エージェントを使用した二足歩行ロボットの学習
Simscape Multibody でモデル化された二足歩行ロボットを制御するために、DDPG と TD3 エージェントを比較する。

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.

Train DQN Agent for Lane Keeping Assist
Train a DQN agent for a lane keeping assist application.
アダプティブクルーズコントロール用の DDPG エージェントの学習
アダプティブクルーズコントロールアプリケーション用の DDPG エージェントに学習させる。
経路追従制御用の DDPG エージェントの学習
レーン追従制御アプリケーション用に、DDPG エージェントの学習を実行する。
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. This problem is known in the literature as goal-based wealth management (GBWM). In GBWM, risk is not necessarily measured using the standard deviation, the value-at-risk, or any other common risk metric. Instead, risk is understood as the likelihood of not attaining an investor's goal. This alternative concept of risk implies that, sometimes, in order to increase the probability of attaining an investor’s goal, the optimal portfolio’s traditional risk (that is, standard deviation) must increase if the portfolio is underfunded. In other words, for the investor’s view of risk to decrease, the traditional view of risk must increase if the portfolio’s wealth is too low.
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.