Applications

Examples of how to apply reinforcement learning

Reinforcement learning can be applied to a variety of problems in different fields, such as control, robotics, scheduling, optimization, and finance. Here are some examples.

Tutorials

Train Agents to Balance Cart-Pole System

Train DQN Agent to Balance Cart-Pole System
Train a DQN agent to balance a cart-pole system modeled in MATLAB^®.
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.

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 pendulum modeled in Simulink^®.
Train DDPG Agent to Swing Up and Balance Pendulum
Train a DDPG agent to balance a pendulum modeled in Simulink.
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™.

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 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.

Train Agents to Perform Control Tasks

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.
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 Agents to Control Robots

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.
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.

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 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 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.

Other Applications

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 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.