Reinforcement Learning Toolbox is a MathWorks product for creating and training reinforcement learning policies, including deep neural network controllers, within the MATLAB and Simulink environment. Engineers build agents that learn control strategies by interacting with environment models, and the tooling covers the full workflow from defining environments and reward functions through to training algorithms and policy evaluation.

Trained policies can be deployed to embedded control hardware and to autonomous systems through MathWorks code generation, which lets teams move a learned controller from simulation onto real equipment. The toolbox is used for control design in areas such as process, automotive and aerospace engineering, where a policy is trained against a simulated plant model before it runs on the physical system.