DeepMeditator

996

996科研和科研之外

DeepMeditator.github.io

DRL_papernotes

Notes and comments about Deep Reinforcement Learning papers

dynamicslearn

Working directory for dynamics learning for experimental robots.

dynamicsPID

Provides use of GP and BO to find optimal PID inputs for robots such as CrazyFlie. Models may be used.

HEBO

Bayesian optimisation library developped by Huawei Noah's Ark Library

IRL-Toolkit

IRL Toolkit developed by Sergey Levine (Taken from https://graphics.stanford.edu/projects/gpirl/)

Probabilistic-Backpropagation

Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.

reward-learning-rl

[RSS 2019] End-to-End Robotic Reinforcement Learning without Reward Engineering

Stein-Variational-Gradient-Descent

code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"

DeepMeditator/996

996科研和科研之外

DeepMeditator/DeepMeditator.github.io

DeepMeditator/DRL_papernotes

Notes and comments about Deep Reinforcement Learning papers

DeepMeditator/dynamicslearn

Working directory for dynamics learning for experimental robots.

DeepMeditator/dynamicsPID

Provides use of GP and BO to find optimal PID inputs for robots such as CrazyFlie. Models may be used.

DeepMeditator/HEBO

Bayesian optimisation library developped by Huawei Noah's Ark Library

DeepMeditator/IRL-Toolkit

IRL Toolkit developed by Sergey Levine (Taken from https://graphics.stanford.edu/projects/gpirl/)

DeepMeditator/Probabilistic-Backpropagation

Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.

DeepMeditator/reward-learning-rl

[RSS 2019] End-to-End Robotic Reinforcement Learning without Reward Engineering

DeepMeditator/Stein-Variational-Gradient-Descent

code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"

DeepMeditator/trajopt

Trajectory optimization algorithms for robotic control.