Reinforcement Learning Toolbox
Design and train policies using reinforcement learning
Reinforcement Learning Algorithms
Create agents using deep Q-network (DQN), deep deterministic policy gradient (DDPG), proximal policy optimization (PPO), and other built-in algorithms. Use templates to develop custom agents for training policies.
Reinforcement Learning Designer App
Interactively design, train, and simulate reinforcement learning agents. Export trained agents to MATLAB for further use and deployment.
Policy and Value Function Representation Using Deep Neural Networks
For complex systems with large state-action spaces, define deep neural network policies programmatically, using layers from Deep Learning Toolbox, or interactively, with Deep Network Designer. Alternatively, use the default network architecture suggested by the toolbox. Initialize the policy using imitation learning to accelerate training. Import and export ONNX models for interoperability with other deep learning frameworks.
Single- and Multi-Agent Reinforcement Learning in Simulink
Create and train reinforcement learning agents in Simulink with the RL Agent block. Train multiple agents simultaneously (multi-agent reinforcement learning) in Simulink using multiple instances of the RL Agent block.
Simulink and Simscape Environments
Use Simulink and Simscape™ to create a model of an environment. Specify the observation, action, and reward signals within the model.
Use MATLAB functions and classes to model an environment. Specify observation, action, and reward variables within the MATLAB file.