To learn an optimal policy, a reinforcement learning agent interacts with the environment through a repeated trial-and-error process. During training, the agent tunes the parameters of its policy representation to maximize the long-term reward. Reinforcement Learning Toolbox™ software provides functions for training agents and validating the training results through simulation. For more information, see Train Reinforcement Learning Agents.
Reinforcement Learning Designer | Design, train, and simulate reinforcement learning agents |
train | Train reinforcement learning agents within a specified environment |
rlTrainingOptions | Options for training reinforcement learning agents |
sim | Simulate trained reinforcement learning agents within specified environment |
rlSimulationOptions | Options for simulating a reinforcement learning agent within an environment |
inspectTrainingResult | Plot training information from a previous training session |
RL Agent | Reinforcement learning agent |
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.
Design and Train Agent Using Reinforcement Learning Designer
Design and train a DQN agent for a cart-pole system using the Reinforcement Learning Designer app.
Specify Simulation Options in Reinforcement Learning Designer
Interactively specify options for simulating reinforcement learning agents.
Specify Training Options in Reinforcement Learning Designer
Interactively specify options for training reinforcement learning agents.
Train Agents Using Parallel Computing and GPUs
Accelerate agent training by running simulations in parallel on multiple cores, GPUs, clusters or cloud resources.
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 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 with Baseline to Control Double Integrator System
Train a policy gradient with a baseline to control a double integrator system modeled in MATLAB.
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 PG Agent to Balance Cart-Pole System
Train a policy gradient agent to balance a cart-pole 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 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™.
Train Multiple Agents to Perform Collaborative Task
Train two PPO agents to collaboratively move an object.
Train Multiple Agents for Area Coverage
Train three PPO agents to explore a grid-world environment in a collaborative-competitive manner.
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.
Imitate MPC Controller for Lane Keeping 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.
Train a custom LQR agent.
Train Reinforcement Learning Policy Using Custom Training Loop
Train a reinforcement learning policy using your own custom training algorithm.
Create Agent for Custom Reinforcement Learning Algorithm
Create agent for custom reinforcement learning algorithm.