Training and Simulation Basics

Train Reinforcement Learning Agents

Find the optimal policy by training your agent within a specified environment.

Train Reinforcement Learning Agent in Basic Grid World

Train Q-learning and SARSA agents to solve a grid world in MATLAB^®.

Train Reinforcement Learning Agent in MDP Environment

Train a reinforcement learning agent in a generic Markov decision process environment.

Create Simulink Environment and Train Agent

Train a controller using reinforcement learning with a plant modeled in Simulink^® as the training environment.

Parallel Computing

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.

Train Agents in MATLAB Environments

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 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 to Balance Cart-Pole System

Train a policy gradient agent to balance a cart-pole 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 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™.

Train Agents in Simulink Environments

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™.

Imitation Learning

Imitate MPC Controller for Lane Keep Assist

Train a deep neural network to imitate the behavior of a model predictive controller.

Imitate Nonlinear MPC Controller for Flying Robot

Train a deep neural network to imitate the behavior of a nonlinear model predictive controller.

Train DDPG Agent with Pretrained Actor Network

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

Custom Agents and Training Algorithms

Train Custom LQR Agent

Train an agent that uses a custom reinforcement learning algorithm.

Train Reinforcement Learning Policy Using Custom Training Loop

Train a reinforcement learning policy using your own custom training algorithm.