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Reinforcement Learning Designer App in MATLAB

Reinforcement Learning MATLAB Toolbox 

Type of machine learning that trains an ‘agent’ through trial  &  error  interactions  with  an  environment

How does reinforcement learning training work?

How do I set up and solve a reinforcement learning problem?


Reinforcement learning toolbox Introduced in MATLAB R2019a 


Features

  • Built-in and custom Reinforcement Learning Algorithms
  • Environment modeling in MATLAB and Simulink
  • Existing Scripts and model can be used
  • Deep Learning Toolbox support for representing policies
  • Training acceleration with Parallel Computing Toolbox and MATLAB Parallel Servers
  • Deployment of trained Policies Reference Examples to get started

MATLAB Toolbox GUI

Reinforcement Learning Designer App 

(Introduced in MATLAB R2021a)

How to Open App:

  • MATLAB Toolstrip: On the Apps tab, under Machine Learning and Deep Learning, click the app icon.
  • MATLAB command prompt: Enter reinforcement Learning Designer.

Using this app, you can:

  • Import an existing environment from the MATLAB® workspace or create a predefined environment. Automatically create or import an agent for your environment (DQN, DDPG, TD3, SAC, and PPO agents are supported).
  • Train and simulate the agent
  • against the environment. Analyze simulation results and refine your agent parameters. Export the final agent to the
  • MATLAB workspace for further use and deployment.





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