Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments ... Currently, real-world applications of ...

This framework, also referred to as RL with/using MPC, was first proposed in and has so far been shown effective in various applications, with different learning algorithms and more sound theory, e.g.

In this paper, a design method for PID control based on Gaussian reinforcement learning is proposed to find the optimal policy for linear quadratic tracking of an unknown system. Firstly, the ...

Autonomous underwater vehicle (AUV) is becoming increasingly important to perform underwater tasks. Model-free reinforcement learning was applied for AUV path following, but is usually inefficient in ...

In this research, we focus on developing a reinforcement learning system for a challenging task: real autonomous ship control, with difficulties arising from uncertainties due to the complex ocean ...

"Deep reinforcement learning builds a model of system dynamics, enabling real-time control—something traditional methods like model predictive control often struggle to achieve due to the repetitive ...

Learn how to incorporate feedback and learning in MPC to optimize complex and uncertain processes using different methods, such as online identification, adaptive control, and reinforcement learning.