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Found 5 papers, 1 papers with code
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning
Its high computational complexity results in high power consumption from the control algorithm, which could account for a significant share of the energy resources in battery-powered embedded systems.
Reinforcement Learning of the Prediction Horizon in Model Predictive Control
Model predictive control (MPC) is a powerful trajectory optimization control technique capable of controlling complex nonlinear systems while respecting system constraints and ensuring safe operation.
Optimization of the Model Predictive Control Update Interval Using Reinforcement Learning
In control applications there is often a compromise that needs to be made with regards to the complexity and performance of the controller and the computational resources that are available.
Linear Antisymmetric Recurrent Neural Networks
Recurrent Neural Networks (RNNs) have a form of memory where the output from a node at one timestep is fed back as input the next timestep in addition to data from the previous layer.
Accelerating Reinforcement Learning with Suboptimal Guidance
Reinforcement Learning in domains with sparse rewards is a difficult problem, and a large part of the training process is often spent searching the state space in a more or less random fashion for any learning signals.
