Autonomous and Control Systems
R2026bExtend deep learning workflows with reinforcement learning, predictive
maintenance, and autonomous navigation applications
Use Deep Learning Toolbox™ to incorporate deep learning in reinforcement learning, predictive maintenance, and autonomous navigation applications.
Categories
- Reinforcement Learning
Train deep neural network agents by interacting with an unknown dynamic environment
- Predictive Maintenance
Extend deep learning workflows with predictive maintenance applications
- Autonomous Navigation
Extend deep learning workflows with autonomous navigation applications
Featured Examples
Train DDPG Agent to Control Two-Thruster Sliding Vehicle
Train a DDPG agent to control a robot sliding over a frictionless 2-D plane.
Train Hybrid SAC Agent for Path-Following Control
Train a hybrid SAC agent for lane following control.
Train Biped Robot to Walk Using Reinforcement Learning Agents
Compare DDPG and TD3 agent for the control a biped walking robot modeled in Simscape™ Multibody™.
Generate Synthetic Signals Using Conditional GAN
Use a conditional generative adversarial network to produce synthetic signals.
Chemical Process Fault Detection Using Deep Learning
Use simulation data to train a neural network than can detect faults in a chemical process.
Rolling Element Bearing Fault Diagnosis Using Deep Learning
Perform fault diagnosis of a rolling element bearing using a deep learning approach.
