#LEARN TO FLY 3 CODES 2020 HOW TO#
#LEARN TO FLY 3 CODES 2020 SERIES#

Fuel consumption (specific fuel consumption estimation and plane mass variation due to consumed fuel.Drag (due to air density variation with altitude and due to the angle of attack variations).Lift and drag coefficient (variations due to angle of attack, including stall and variations due to Mach number impact on lift and drag).Thrust (Variations due to altitude and air rarefaction).I have chosen to simulate an Airbus A320 because of its wide use across the world as well as the number of data available for this aircraft (The illustrations will still be made using my wooden toy plane but the figures will be based on the actual plane, such figures can be found in ). Let’s create our airplane simulation, computing the airplane’s next position, velocity, and acceleration based on its previous position, velocity, and acceleration of the plane. I am not going to get too technical about flight mechanics and the origin of lift in this article, but if you wish to know more about this subject (Bernoulli’s Principle, Newton’s Laws and Navier-Stokes equations) I highly recommend you watch this video from David Louapre (I have added English captions for the occasion, they are currently awaiting approval) : I. In the second article ( here ), we’ll talk about the rest of the environment (states, actions, and rewards) and the reinforcement learning agent (model selection, hyperparameters optimization, and agent training and testing). We will basically be creating a basic airplane simulation. In this first article, we’ll define the transition function allowing the environment to compute the next state based on the previous state and the agent actions: in our case computing the airplane’s next position, velocity, and acceleration based on its current position, velocity and acceleration and the pilot’s action (thrust value and pitch angle). The agent will interact with the environment, selecting actions, and observing their results (new states and rewards) while trying to optimize its return (the sum of its rewards).

#LEARN TO FLY 3 CODES 2020 HOW TO#

Reinforcement Learning is the branch of machine Learning (making algorithms learn how to do things rather than telling them how to do it) that deals with the training of an artificial intelligence through an action-and-reward process. Nowadays AI is used in all sorts of applications from biped robots to autonomous cars, but so far no AI Airplane has been seen, let’s change that!

#LEARN TO FLY 3 CODES 2020 SERIES#

In this series of articles, I am going to write about my journey into using artificial intelligence to make a plane fly. Continued abuse of our services will cause your IP address to be blocked indefinitely.From Icarus burning his wings to the Wright brothers soaring through the sky, it took mankind thousands of years to learn how to fly, but how long will it take an AI to do the same? Introduction Please fill out the CAPTCHA below and then click the button to indicate that you agree to these terms.

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Learn to fly 3 codes 2020