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 the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strategies to enhance ...

"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 ...

All machine learning methods rely on a series of hyperparameters such as the number of epochs, batch size, choice of activation function, and the number of network hidden layers. Due to the complex ...

Efficient Model-Based Deep Reinforcement Learning with Predictive Control: Developed a Model-Based RL algorithm using MPC, achieving convergence in 200 episodes (best case) and 1000 episodes on ...

In this paper, a novel Model Predictive Control (MPC) guided Reinforcement Learning Control (MP-RLC) scheme is proposed for the process control. In this scheme, Model predictive control is directly ...