samholt / NeuralLaplaceControlLinks
Neural Laplace Control for Continuous-time Delayed Systems - an offline RL method combining Neural Laplace dynamics model and MPC planner to achieve near-expert policy performance in environments with irregular time intervals and an unknown constant delay.
☆13Updated 2 years ago
Alternatives and similar repositories for NeuralLaplaceControl
Users that are interested in NeuralLaplaceControl are comparing it to the libraries listed below
Sorting:
- Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…☆42Updated 6 years ago
- IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control☆48Updated 4 years ago
- [ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control☆91Updated 4 years ago
- DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control☆23Updated 5 years ago
- Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.☆98Updated 3 years ago
- Code for the paper, "Industrial, large-scale model predictive control with structured neural networks."☆19Updated 3 years ago
- A framework for neural network control of dynamical systems over graphs.☆56Updated 3 years ago
- Experiment code for "Continuous-Time Model-Based Reinforcement Learning"☆54Updated last year
- ☆19Updated 5 years ago
- Koopman Kernels for Learning Dynamical Systems from Trajectory Data☆31Updated last year
- A PyTorch library for all things nonlinear control and reinforcement learning.☆47Updated 4 years ago
- MISO: Learning Multiple Initial Solutions to Optimization Problems☆15Updated 11 months ago
- Companion code to "Learning Stable Deep Dynamics Models" (Manek and Kolter, 2019)☆33Updated 5 years ago
- Code repo for ICLR paper: Optimal Control Via Neural Networks: A Convex Approach☆84Updated 6 years ago
- Consistent Koopman Autoencoders☆74Updated 2 years ago
- Official implementation for our paper "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"☆19Updated 3 years ago
- ☆73Updated 5 years ago
- Use deep learning to learn Koopman operator and LQR for optimal control☆16Updated 5 years ago
- ☆43Updated 2 years ago
- A Neural Network Approach for Real-Time High-Dimensional Optimal Control☆27Updated 3 years ago
- Data-driven Koopman control theory applied to reinforcement learning!☆33Updated 2 years ago
- Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gy…☆145Updated 2 years ago
- Almost Surely Stable Deep Dynamics [NeurIPS 2020]☆13Updated 2 years ago
- Literature and code for inverse reinforcement leanring research☆29Updated 5 years ago
- Code base for NeurIPS 2022 paper Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation.☆11Updated 2 years ago
- Model-based reinforcement learning using CEM, MPC and PETS☆16Updated 5 years ago
- The Wasserstein Distance and Optimal Transport Map of Gaussian Processes☆52Updated 5 years ago
- Library for simulation of nonlinear control systems, control design, and Lyapunov-based learning.☆41Updated 2 years ago
- Companion code for RSS 2020 paper: "Active Preference-Based Gaussian Process Regression for Reward Learning"☆39Updated last year
- ☆14Updated 2 years ago