Displaying 1 to 5 from 5 results

essence - AutoDiff DAG constructor, built on numpy and Cython

  •    Python

A directed acyclic computational graph builder, built from scratch on numpy and C, with auto-differentiation supported. This was not just another deep learning library, its clean code base was supposed to be read. Great for any one who want to learn about Backprop design in deep learning libraries.

ilqr - Iterative Linear Quadratic Regulator with auto-differentiatiable dynamics models

  •    Python

This is an implementation of the Iterative Linear Quadratic Regulator (iLQR) for non-linear trajectory optimization based on Yuval Tassa's paper. It is compatible with both Python 2 and 3 and has built-in support for auto-differentiating both the dynamics model and the cost function using Theano.

pddp - WIP implementation of Probabilistic Differential Dynamic Programming in PyTorch

  •    Jupyter

Probabilistic Differential Dynamic Programming (PDDP) is a data-driven, probabilistic trajectory optimization framework for systems with unknown dynamics. This is an implementation of Yunpeng Pan and Evangelos A. Theodorou's paper in PyTorch, [1]. This is a work in progress and does not work/converge as is yet.

safe-control-gym - PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning

  •    Python

Physics-based CartPole and Quadrotor Gym environments (using PyBullet) with symbolic a priori dynamics (using CasADi) for learning-based control, and model-free and model-based reinforcement learning (RL). These environments include (and evaluate) symbolic safety constraints and implement input, parameter, and dynamics disturbances to test the robustness and generalizability of control approaches.

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