sebastian-east / dare-torchLinks
Implementation of a differetiable discrete-time algebraic Riccati equation (DARE) solver in PyTorch.
☆11Updated 2 years ago
Alternatives and similar repositories for dare-torch
Users that are interested in dare-torch are comparing it to the libraries listed below
Sorting:
- Enforcing robust control guarantees within neural network policies☆53Updated 4 years ago
- Implementation of robust adaptive control methods for the linear quadratic regulator☆38Updated 3 years ago
- Source code for the examples accompanying the paper "Learning convex optimization control policies."☆84Updated 2 years ago
- Safe Exploration with MPC and Gaussian process models☆90Updated 4 years ago
- Companion code to "Learning Stable Deep Dynamics Models" (Manek and Kolter, 2019)☆33Updated 5 years ago
- Safe learning of regions of attraction in uncertain, nonlinear systems with Gaussian processes☆39Updated 5 years ago
- A toolbox for trajectory optimization of dynamical systems☆53Updated 2 years ago
- Google AI Princeton control framework☆38Updated 4 years ago
- Codebase associated with paper "Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control"☆24Updated 4 years ago
- ☆30Updated last year
- A PyTorch library for all things nonlinear control and reinforcement learning.☆46Updated 3 years ago
- Input Inference for Control (i2c), a control-as-inference framework for optimal control☆23Updated last year
- A Neural Network Approach for Real-Time High-Dimensional Optimal Control☆26Updated 2 years ago
- ☆24Updated 4 years ago
- Myriad is a real-world testbed that aims to bridge trajectory optimization and deep learning.☆66Updated last year
- Pytorch implementation of Model Predictive Control with learned models☆30Updated 4 years ago
- IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control☆45Updated 4 years ago
- Model-based Control using Koopman Operators☆51Updated 4 years ago
- This is the code repository for the DuSt-MPC paper, published at Robotics: Science and Systems (RSS) 2021.☆25Updated 3 years ago
- Julia package for computing Hamilton-Jacobi Reachability of optimal 2-player (control/disturbance) differential games via Hopf optimizati…☆18Updated 2 months ago
- Continuous-Time/State/Action Fitted Value Iteration via Hamilton-Jacobi-Bellman (HJB)☆15Updated 3 years ago
- SLSpy provides a Python-based framework to design and simulate model-based control systems, especially for system level synthesis (SLS) m…☆17Updated last year
- Codes for designing Neural Contraction Metrics (NCMs)☆33Updated 4 years ago
- Use deep learning to learn Koopman operator and LQR for optimal control☆16Updated 4 years ago
- Model-based reinforcement learning in TensorFlow☆56Updated 3 years ago
- Neural ODEs as Feedback Policies for Nonlinear Optimal Control (IFAC 2023) https://doi.org/10.1016/j.ifacol.2023.10.1248☆16Updated last year
- ☆21Updated 6 years ago
- Code needed to reproduce the examples found in "Learning Control Barrier Functions from Expert Demonstrations," by A. Robey, H. Hu, L. Li…☆65Updated last year
- This repository contains the code for RL for POMDPs through learning an Approximate Information State.☆21Updated 3 years ago
- A library to benchmark reinforcement learning algorithms☆21Updated 7 years ago