Navigation: Deep Q Networks

Reinforcement Learning

Trained Agent using DQN

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

About Deep Reinforcement Learning

Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. They can start from a blank slate, and under the right conditions they achieve superhuman performance. Like a child incentivized by spankings and candy, these algorithms are penalized when they make the wrong decisions and rewarded when they make the right ones – this is reinforcement.

This project implement a Value Based method called Deep Q-Networks

Karthik Bhaskar
Karthik Bhaskar
Machine Learning Researcher | Data Scientist | Software Engineer

Machine Learning Researcher | Software Engineer | Vector Institute | University of Toronto | University Health Network