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George Fragkos's Projects

automatedstocktrading-deepq-learning icon automatedstocktrading-deepq-learning

Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.

caliper icon caliper

A blockchain benchmark framework to measure performance of multiple blockchain solutions

car-charge-handler icon car-charge-handler

This repository collect al the necessary information and code to run a car-charger with a RaspBerry Pi. It use openEVSE, OCPP and Blockchain and some hardware

cobs icon cobs

OPE Tools based on Empirical Study of Off Policy Policy Estimation paper.

comnetsemu icon comnetsemu

A holistic testbed/emulator for the book: Computing in Communication Networks: From Theory to Practice (2020)

ganache-cli icon ganache-cli

Fast Ethereum RPC client for testing and development

geofragkos.github.io icon geofragkos.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

iso15118 icon iso15118

Implementation of the ISO 15118 Communication Protocol (-2, -20, -8)

ldapscripts icon ldapscripts

Simple shell scripts to handle POSIX entries in an LDAP directory

minimal icon minimal

Personal blog theme powered by Hugo

neovis.js icon neovis.js

Neo4j + vis.js = neovis.js. Graph visualizations in the browser with data from Neo4j.

ocpp icon ocpp

Python implementation of the Open Charge Point Protocol (OCPP).

rbac-sc icon rbac-sc

Solidity Code for the RBAC-SC smart contract

reinforcement-learning icon reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

responsible-ai-toolbox icon responsible-ai-toolbox

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

rl-algorithms icon rl-algorithms

Implementation & test of RL algorithms on MiniHack environment

sacmat2018 icon sacmat2018

Dataset and experimental results of the manuscript "Parametric RBAC Maintenance via Max-SAT" published to SACMAT Conference 2018.

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