ahmadsuleman Goto Github PK

activity_rec_ml-lstm

Activity Recognition using Temporal Optical Flow Convolutional Features and Multi-Layer LSTM

ahmadsuleman

Config files for my GitHub profile.

alphapose

Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

artificial-intelligence-and-data-science-pro

Regularly Updated | Collection of the best Data Science and AI Material from the Web | Covering Everything from Books, Courses along with Material, Research Papers, and Interview Prep to Cheatsheet

artificial-intelligence-berkeley-cs188

Learned about search problems (A*, CSP, minimax), reinforcement learning, bayes nets, hidden markov models, and machine learning

assistive-gym

Assistive Gym, a physics-based simulation framework for physical human-robot interaction and robotic assistance.

auto_annotate

Automate approach to label images for object detection using TensorFlow

bugbot

A open-source 2D bug robot simulator.

chainerrl

ChainerRL is a deep reinforcement learning library built on top of Chainer.

citylearn

Official reinforcement learning environment for demand response and load shaping

computer-vision-using-deep-learning

source code

computervision-recipes

Best Practices, code samples, and documentation for Computer Vision.

continuous-online-video-classification-blog

Code that accompanies a blog posts on continuous online video classification with TensorFlow, Inception and a Raspberry Pi

conv_snn

Code for "Convolutional spiking neural networks (SNN) for spatio-temporal feature extraction" paper

crar-tower

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

deep-reinforcement-learning

Repo for the Deep Reinforcement Learning Nanodegree program

deep-reinforcement-learning-hands-on

Hands-on Deep Reinforcement Learning, published by Packt

deep-reinforcement-learning-with-pytorch

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

deepreinforcementlearninginaction

Code from the Deep Reinforcement Learning in Action book from Manning, Inc

denki-uc

Stochastic unit commitment model in python

diceroller_game

dl-colab-notebooks

Try out deep learning models online on Google Colab

docs

TensorFlow documentation

drl-for-microgrid-energy-management

We study the performance of various deep reinforcement learning algorithms for the problem of microgrid’s energy management system. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a population of thermostatically controlled loads, a population of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate between the different sources of flexibility by defining the priority resources, the direct demand control signals and the electricity prices. Seven deep reinforcement learning algorithms are implemented and empirically compared in this paper. The numerical results show a significant difference between the different deep reinforcement learning algorithms in their ability to converge to optimal policies. By adding an experience replay and a second semi-deterministic training phase to the well-known Asynchronous advantage actor critic algorithm, we achieved considerably better performance and converged to superior policies in terms of energy efficiency and economic value.

emdp

Easy MDPs and grid worlds with accessible transition dynamics to do exact calculations

firstone

This is just to know how to use git hub

github-slideshow

A robot powered training repository :robot:

gridworld

OpenAI gym-based algorithm for the grid world problem

gym

A toolkit for developing and comparing reinforcement learning algorithms.