Reinforcement Learning Toolbox


Reinforcement Learning Toolbox

Design and train policies using reinforcement learning

Reinforcement Learning Agents

Create model-free and model-based reinforcement learning agents using popular algorithms such as DQN, PPO, and SAC. Alternatively, develop your own custom algorithms with provided templates. Use RL Agent block to bring your agents into Simulink.

Reinforcement Learning Designer App

Interactively design, train, and simulate reinforcement learning agents. Export trained agents to MATLAB for further use and deployment.

Reward Signals

Create reward signals that measure how successful the agent is at achieving its goal. Automatically generate reward functions from control specifications defined in Model Predictive Control Toolbox or Simulink Design Optimization.

Policy Representation

Get started quickly by using neural network architectures suggested by the toolbox. Alternatively, explore lookup tables, or define neural network policies manually, with Deep Learning Toolbox layers, and Deep Network Designer app.

Reinforcement Learning Training

Train agents through interactions with an environment or using existing data. Explore single- and multi-agent training. Log and view training data, and monitor progress as you go.

Distributed Computing

Speed up training using multicore computers, cloud resources, or compute clusters with Parallel Computing Toolbox and MATLAB Parallel Server. Leverage GPUs to accelerate operations such as gradient computation and prediction.

Environment Modeling

Model environments that interact seamlessly with the reinforcement learning agents using MATLAB and Simulink. Interface with third-party modeling tools.

Code Generation and Deployment

Automatically generate C/C++ and CUDA code from trained policies for deployment to embedded devices. Use MATLAB Compiler and MATLAB Production Server to deploy trained policies to production systems as standalone applications, C/C++ shared libraries, and more.

Reference Examples

Design controllers and decision-making algorithms for robotics, automated driving, calibration, scheduling, and other applications. Consult our reference examples to get started quickly.

“5G is a critical infrastructure that we must protect from adversarial attacks. Reinforcement Learning Toolbox allows us to quickly assess 5G vulnerabilities and identify mitigation methods.”

Ambrose Kam, Lockheed Martin

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