Reinforcement Learning Toolbox

Design and train policies using reinforcement learning

Reinforcement Learning Toolbox™ provides functions and blocks for training policies using reinforcement learning algorithms including DQN, A2C, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex systems such as robots and autonomous systems. You can implement the policies using deep neural networks, polynomials, or look-up tables.

The toolbox lets you train policies by enabling them to interact with environments represented by MATLAB® or Simulink® models. You can evaluate algorithms, experiment with hyperparameter settings, and monitor training progress. To improve training performance, you can run simulations in parallel on the cloud, computer clusters, and GPUs (with Parallel Computing Toolbox™ and MATLAB Parallel Server™).

Through the ONNX™ model format, existing policies can be imported from deep learning frameworks such as TensorFlow™ Keras and PyTorch (with Deep Learning Toolbox™). You can generate optimized C, C++, and CUDA code to deploy trained policies on microcontrollers and GPUs.

The toolbox includes reference examples for using reinforcement learning to design controllers for robotics and automated driving applications.

Getting Started

Learn the basics of Reinforcement Learning Toolbox

MATLAB Environments

Model reinforcement learning environment dynamics using MATLAB

Simulink Environments

Model reinforcement learning environment dynamics using Simulink models

Policies and Value Functions

Define policy and value function representations, such as deep neural networks and Q tables

Agents

Create and configure reinforcement learning agents using common algorithms, such as SARSA, DQN, DDPG, and A2C

Training and Validation

Train and simulate reinforcement learning agents

Policy Deployment

Code generation and deployment of trained policies