Trajectory optimization (indirect with iLQR, direct with SQP), model predictive control, and additional tools for quantum optimal control.
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Updated
Jul 25, 2023 - Python
Trajectory optimization (indirect with iLQR, direct with SQP), model predictive control, and additional tools for quantum optimal control.
First homework for the RL class
Repository of Reinforcement Learning projects done during the course @sapienza
Non-linear trajectory optimization via iLQR/DDP.
MPC, iLQR, Stanley, Pure Pursuit Controllers in AWSIM using ROS2
Optimal control solver implemented in Python. SymPy for symbolic differentiation and Numba for fast computation.
Thesis: Application of Reinforcement Learning for the Control of Nonlinear Dynamical Systems
Autonomous racing using ILQR and JAX.
Differential Dynamic Programming python implementation for a cartpole system
Model-based Policy Gradients
An implementation of model-predictive control algorithms using TensorFlow 2
A toolbox for trajectory optimization of dynamical systems
iterative Linear Quadratic Regulator with constraints.
Implementation of the real-time MPC based on iLQR in Carla simulator
Iterative Linear Quadratic Regulator with auto-differentiatiable dynamics models
PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.
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