Emmanouil Tzorakoleftherakis, MathWorks
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.
Reinforcement Learning Toolbox provides functions and blocks that let you implement controllers and decision-making algorithms for autonomous systems such as robots and self-driving cars.
The toolbox enables you to work through all steps of the reinforcement learning workflow, from creating the environment and the agent to policy training and deployment, with MATLAB and Simulink.
Create deep neural network policies and value functions with Deep Network Designer or programmatically with built-in functions.
In addition to neural networks, polynomials and lookup tables are also supported.
Define an agent by combining the policy with built-in training algorithms, such as actor-critic methods or Deep Q network.
You can create environments in both MATLAB and Simulink.
In Simulink, create a model that describes the environment dynamics and reward signal.
Use the Agent block to interface the environment model with the agent.
For MATLAB environments, you may start with provided templates and make modifications as needed.
You can also select from several predefined MATLAB and Simulink environments.
To train an agent, specify training options such as stopping criteria and start the training process using the agent and the environment model.
During training, the Episode manager helps you visually monitor the training progress and provides summary statistics.
After training is complete, you can verify the trained agent with the simulation environment and you can generate CUDA and C/C++ code to deploy the trained policy.
For more information on Reinforcement Learning Toolbox, please refer to the documentation and provided examples.
Get started with a free trial of Reinforcement Learning Toolbox today.
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