Lunar Lander Genetic Algorithm

Summary

A quick overview on how we created a Simple Mars Population size 60 Chance of mutation 5% 20 Generations. Instead of Reinforcement learning, I went ahead to train the agent using DGA which I call, Deep Just a cool visualization I generated while building a solution to the mars Potential 0:00 Introduction 0:39 Rocket NN 1:24 How Rockets Get Better 2:39 Finally So, here's the deal: this video was made in collaboration with a bunch of other cool content creators who all wanted to talk about ... This lecture provides an overview of MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston This ... This video discusses how we reverse-engineered the flight physics to run frame-by-frame simulations, as well as how we made ... Welcome to a new series on evolutionary computation! To start, we'll be introducing Tournament selection, roulette selection, mutation, crossover - all processes used in