Model Predictive Control
162 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find Model Predictive Control models and implementationsMost implemented papers
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance.
Multi-Period Trading via Convex Optimization
The methods we describe in this paper can be thought of as good ways to exploit predictions, no matter how they are made.
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
In this work we propose a deep learning-based strategy for nonlinear model reduction that is inspired by projection-based model reduction where the idea is to identify some optimal low-dimensional representation and evolve it in time.
Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture
We introduce REPRISE, a REtrospective and PRospective Inference SchEme, which learns temporal event-predictive models of dynamical systems.
Differentiable MPC for End-to-end Planning and Control
locuslab/differentiable-mpc
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NeurIPS 2018
We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces.
Interactive Differentiable Simulation
While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables.
Driving in Dense Traffic with Model-Free Reinforcement Learning
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
Deep Dynamics Models for Learning Dexterous Manipulation
Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills.
Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control
An intelligent controller example based on the deep deterministic policy gradient algorithm which controls a series DC motor is presented and compared to a cascaded PI-controller as a baseline for future research.
Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees
pnnl/neuromancer
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We present differentiable predictive control (DPC), a method for learning constrained neural control policies for linear systems with probabilistic performance guarantees.
