[NeurIPS 2024] Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space
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Updated
Oct 17, 2024 - Python
[NeurIPS 2024] Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space
JAX compilation of RDDL description files, and a differentiable planner in JAX.
We compare model-free and model-based methods in the context of battery control.
For the consolidation of my personal model predictive control (MPC) library. Example cases also given.
Gurobi compilation of RDDL description files to mixed-integer programs, and optimization tools.
Wrappers for reinforcement learning algorithms (i.e. stable baselines 3, RLlib) to work with pyRDDLGym.
Sidney's Technical Blog
Docker files for connecting the PROST planner with pyRDDLGym.
An open-source systems and controls toolbox for Python3
Model-based Calibration of Multiple Injections for a CI engine
Height Control and Optimal Torque Planning for Jumping with Wheeled-Bipedal Robots
Sketches of model-based control ideas for gym classic control
Model-based Control using Koopman Operators
PyTorch implementation of "Learning Stable Deep Dynamics Models" (https://papers.nips.cc/paper/9292-learning-stable-deep-dynamics-models), with extensions to controlled dynamical systems.
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