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 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.
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