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.

Comments

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