A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes

Keyphrases

• Active Control 100%
• Active Learning 100%
• Gaussian Process 100%
• Receding Horizon Strategy 100%
• Multiple Steps 33%
• Learning Objectives 33%
• System Dynamics 33%
• Programming Algorithm 33%
• Dynamical Systems 33%
• Optimization Problem 33%
• Control Problem 33%
• Data Quality 33%
• Learning Problems 33%
• Model Learning 33%
• First-order Approximation 33%
• Receding Horizon 33%
• Sequential Convex Programming 33%
• Log-determinant 33%
• Gaussian Process Prediction 33%
• Nonconvex Optimization 33%
• Posterior Covariance Matrix 33%
• Nonconvex Functions 33%
• Differential Entropy 33%
• Autonomous Cars 33%
• Conditional Differential 33%

Engineering

• Gaussians 100%
• Optimisation Problem 50%
• Simulation Result 25%
• Quality Data 25%
• Systems Dynamics 25%
• Covariance Matrix 25%
• Differential Entropy 25%
• Convex Function 25%
• Approximation Order 25%

Mathematics

• Gaussian Process 100%
• Multiple Step 25%
• Conditionals 25%
• Dynamical System 25%
• Convex Programming 25%
• Covariance Matrix 25%
• Control Problems 25%
• Convex Function 25%
• Approximation Order 25%