Applications

Tutorials

Train Agents to Balance Cart-Pole System

• Train DQN Agent to Balance Discrete Cart-Pole System
  Train a DQN agent to balance discrete action space cart-pole system modeled in MATLAB^®.
• Train PG Agent to Balance Discrete Cart-Pole System
  Train a PG agent to balance a discrete action space cart-pole system modeled in MATLAB.
• Train AC Agent to Balance Discrete Cart-Pole System
  Train an 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 continuous action space cart-pole system modeled in Simscape™ Multibody™.
• 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.

Train Agents to Swing Up and Balance Pendulum

• Train DQN Agent to Swing Up and Balance Pendulum
  Train a DQN agent to swing up and balance a discrete action space pendulum modeled in Simulink^®.
• Train DDPG Agent to Swing Up and Balance Pendulum
  Train a DDPG agent to balance a continuous action space pendulum modeled in Simulink.
• Train DDPG Agent to Swing Up and Balance Pendulum with Bus Signal
  Train a DDPG agent to balance a continuous action space 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™.

Train Agents to Control Double Integrator System

• 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 Custom Networks to Control Discrete Double Integrator
  Train a PG agent with a baseline to control a discrete action space double integrator system modeled in MATLAB.

Train Agents to Perform Control Tasks

• Control Water Level in a Tank Using a DDPG Agent
  Train a controller using reinforcement learning with a plant modeled in Simulink as the training environment.
• Tune PI Controller Using Reinforcement Learning
  Tune the gains of a PI controller using a TD3 agent.
• Train SAC Agent for Ball Balance Control
  Train a SAC agent to balance a ball on a flat surface using a robot arm.
• Control Water Level in a Tank Using a DDPG Agent
  Train a controller using reinforcement learning with a plant modeled in Simulink as the training environment.
• 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.
• 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 Agents to Control Robots

• Train DDPG Agent to Control Sliding Robot
  Train a DDPG agent to control a continuous action space flying robot model.
• 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.
• Quadruped Robot Locomotion Using DDPG Agent
  Train a DDPG agent to control a quadruped walking robot modeled in Simscape Multibody.

Generate Rewards from Control Specifications

• 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.

Imitation Learning

• 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 Agents for Automotive Applications

• Train DQN Agent for Lane Keeping Assist
  Train a DQN agent for a lane keeping assist application.
• 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.
• Train DDPG Agent for Adaptive Cruise Control
  Train a DDPG agent for an adaptive cruise control application.
• Train DDPG Agent for Path-Following Control
  Train a DDPG agent for lane following control.
• 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.

Other Applications

• 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
  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 Model Based Policy Optimization Agents

• 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.

Featured Examples

Identify Vulnerabilities in DC Microgrids

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

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Train Agent to Play Turn-Based Game

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

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Automatic Parking Valet with Unreal Engine Simulation

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

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Quadruped Robot Locomotion Using DDPG Agent

Train a DDPG agent to control a quadruped walking robot modeled in Simscape Multibody.

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