This repository contains my work for Udacity’s Deep Reinforcement Learning Nanodegree For this project, we will work with the Tennis environment.
In this environment, two agents control rackets to bounce a ball over a net.
This project repository contains my work for the Udacity’s Deep Reinforcement Learning Nanodegree Project 2: Continuous Control.
Project’s goal In this environment, a double-jointed arm can move to target locations. A reward of +0.
Abstract This project discuss the transferability of state of the art defense techniques for adversarial examples for deep learning systems in the physical domain. The paper explores using adversarial attacks using the Fast Gradient Sign Method (FGSM), Carlini & Wagner (CW) and DeepFool attacks to generate adversarial images that are given to the classifier as a digital and physically transformed image.
Abstract We used supervised training to create a series of chess engines based on humans play at different levels of skill. We compared them to other engines and to human players and found that self-play trained engines would sometimes behave more human-like than the supervised ones, although we believe this may be due to improper hyperparameter selection.
Abstract A multi-task learning convolutional neural network for the purpose of performing landmark localization and other correlated tasks is studied and analysed in this project. A different and more challenging task around landmark localization than the one implemented originally is studied using a HyperFace architecture.
Deep Reinforcement Learning : Navigation This project repository contains my work for the Udacity’s Deep Reinforcement Learning Nanodegree Project 1: Navigation.
Project’s goal In this project, the goal is to train an agent to navigate a virtual world and collect as many yellow bananas as possible while avoiding blue bananas