Reinforcement Learning Toolbox™ provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
The toolbox lets you represent policies and value functions using deep neural networks or look-up tables and train them through interactions with environments modeled in MATLAB® or Simulink. You can evaluate the single- or multi-agent reinforcement learning algorithms provided in the toolbox or develop your own. You can experiment with hyperparameter settings, monitor training progress, and simulate trained agents either interactively through the app or programmatically. To improve training performance, simulations can be run in parallel on multiple CPUs, GPUs, computer clusters, and the cloud (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 to help you get started.
Train Q-learning and SARSA agents to solve a grid world in MATLAB.
Train a reinforcement learning agent in a generic Markov decision process environment.
Train a controller using reinforcement learning with a plant modeled in Simulink as the training environment.
Design and train a DQN agent for a cart-pole system using the Reinforcement Learning Designer app.
Reinforcement learning is a goal-directed computational approach where a computer learns to perform a task by interacting with an uncertain dynamic environment.
You can train a reinforcement learning agent to control an unknown plant.
Reinforcement Learning Onramp
This free, four-hour tutorial provides an interactive introduction to reinforcement learning.