🖥️ FINISHING MY FIRST MACHINE LEARNING GAME! (3/4)

all right welcome back for this final installment no more fun and games folks since last episode I've added a simple course builder using hex shapes and now we have the ability to make a large number of courses very quickly so real quick before we begin let's just make this arbitrary course and test the forest that we last left off with on part 2 to see how will he do yeah okay let's get into this let's finally try and make force to any course running master that he's always dreamt of becoming and to do this we first need a goal so how about this I've designed a campaign mode of 40 PC in difficulty courses and one final course that is the monster of a course previewed in part one and two if force can successfully run two laps on each course in both directions and who will be dubbed the ami course running master so here are my training tactics looking at the campaign maps I think the first two or three are gonna be pretty easy but it's really the last couple that we need to worry about they have lots of turns corners confusing intersections and this is about as all I can identify for training purposes so here are three forest brain training tactics that I want to try tactic number one the forest dedicated tactic when I think of training the first thing that comes to mind is the natural process of going from one stage to the next until you're good and that natural process is what inspired for it's dedicated I'm first gonna train for us on a very simple course then make sure that he masters that course then move him on to a more complicated course and I will keep up this process until he is able to be the toughest course that I can throw at him I mean you'd agree this is our most natural processes in life works right so this should be your campaign winner right tactic number two the Forrest Gump tactic I think the most obvious thing to do but maybe not the smartest is to try and design the toughest course possible and then train for us right away on that course the idea is to get forced to master every crevice of that course because if he can handle the toughest course that I can throw at him then hey he should be able to handle any course in the campaign right and tactic number three Forrest calculating this tactic is more of a mathematical approach to training we're going to first rainforest on a circular course I have prophesized that this will give us a surefire thought process that can handle general attorney then will train a completely new for us on a course with a lot of ninety degree or less sharp corners I hope all the sides of course that this will give us a thought process I can hear the sharp corners and then we'll turn the final force on the course with lots of confusing intersections it's my belief that this will give us a thought process that will know what to do with confusing pockets like this and finally will use all three of the Train forests thought processes to train on the toughest course I can make to hopefully make one ultimate trained force once a four is dedicated Forrest Gump and force kak way to Train to the best that I can train them we will then put them to the test live and see if they will be able to beat the campaign dubbing one or maybe even all of them the any course running master and me the greatest trainer of all time I don't know but I'm about to explode from all this excitement to find out well it's before we can start training there's something that we need to take care of first if you remember last episode we left off with two crossover functions that we were unsure which will be better to use for final training and so since the last episode I've added some graphs to get to the bottom of this the green bars indicate an increase in performance the red bars indicate a decrease in performance and the yellow bar indicates the top performance for this training session the left side shows the top fitness per day and the right side shows the average fitness per day so using these graphs let's run some tests and hypothesize which might give us a better chance at success let the training begin oh all right guys after some 12 plus hours of training have you got some great data to show you I've ran both crossover functions five times for 60 in game days each using two different courses total but that's not all there's something that I've only been keeping a secret from you in part two I said that our genetic algorithm will assign a probability to each thought process to indicate their chances to be used for the next day well I was unsure if this was the most optimal way to go about it so I also added another selection function called top to in which every new thought process for the next day is instead based off of only the top two best thought processes so looking at these graphs that's what you're seeing and if it's still a bit confusing imagine a simple punnett square with the two crossover functions on the top and the two selection functions on the left I've tested each combination to try and find out which might be the best and here my takeaways looking at the first graph which visualizes the average top fitness from all five runs per day the slice crossover mixed with the top to selection method performed worse followed by arguably the slice slash probability method in the random crossover / top - selection method and in first place the random slash probability method when I first looked at this data I was shocked and actually a bit offended because I mean based on my quick google search from part two I think nature uses the slice slash probability method so naturally that's what I have my money on no one insults nature except this genetic algorithm I guess but as I took a step back and realize this isn't nature it's a computer simulation over 60 in-game days opposed to billions of years of evolution and that's when this data actually started to make a lot more sense to me as I think the chart shows the slice crossover function is generally quicker at finding solutions but it's also more likely to get stuck in the local minimum if we were to flip this graph upside down I think the local minimum becomes easier to visualize this here is the global minimum and all of these are local minimums now why is the slice crossover function more likely gets stuck in local minimums well if we take a look at our second graph which visualizes the averages of the average Fitness from all five training sessions per day take a second to let that register and continue the peaks and troughs don't stray too far away from each other which i think indicates that the slice function results in most thought processes being similar to each other with only slight variation thus it doesn't explore the search space as well as the random crossover does now if we're to compare these two the peaks and troughs of the random crossover function you'll see that it's averages have major rises and drops which shows that the random crossover is making huge changes to look for new solutions sometimes it finds them and we see major Rises and other times it doesn't find it in fact it increases performance and we see major drops but what's important is that it's exploring the slash crossover is more or less designed to play it safe like someone you only make small safe investments and companies like Apple Microsoft Disney you get it they could become rich from this but it might take them some time to get there while the random crossover function is more like someone who makes big risky investments and startups like napkins on wills soggy cereal restaurant and Donald Trump's second term some investments will be a complete waste of money but others will turn great reward well at least that's what I got from the graphs if you see something else feel free to post in the comments and let's discuss it so with that said it appears that the con for the slice crossover is that it takes a lot longer to have a breakthrough but when it does because it's exploring the search space so meticulously it's Pro is that it will ideally continue to improve until its next breakthrough which pretty much means better accuracy and it appears that the probe the random crossover function is because it's a lot more daring it explores the search space a lot better than the slice crossover but it also appears that this can lead to a loss of progress it can find a good solution be unsure if this is the best solution not make a giant change then drop in performance sometimes not recovering for a very long time or ever as good as it once was to just look at the data from another perspective also zips and data from these two graphs and graphed it using the mean and median from everyday and these graphs point clearly to the random slash probability method being the most efficient way to train force on average the random slash probability method has a higher fitness and also as a higher fitness midpoint which a high fitness midpoint indicates that is huge change method finds better solutions faster but nature don't be fooled you're still number one you just need a more time we still love and support you okay looking at this data which one has your vote I don't know about you I'm for sure going with the random slash probability method however because I ran a poll on Twitter and YouTube and you guys overwhelmingly voted to test both crossover functions and final training and even though it's gonna take double the training time and the data right here in our face is telling us that the random size probability method is the chosen one I have to remember this is for machine learning research so let's do it besides what if I have no idea how to analyze data correctly and random slash probability method actually isn't the chosen one this might be interesting after all but real quick Singh how about the top two selection function performed let's not even waste their time with it so instead of training three forests with only the random slash probability method we're also going to train three more forest using the slice slash probability method and test all six forests on the campaign mode so without further yapping at the mouth let the actual training [Music] [Music] boy that was some intense training but at last we have our six fours now who's ready to put them to the test all right Forrest on your mark get set run [Music] wait [Music] waitwait [Music] oh no it wasn't supposed to end like this we're supposed to dub one of these forces to any course running master ah well guys we tried and frankly that's the end of this project because I'm wearing a belong to more fun machine learning projects however it's not the end of this series I expected to be at the campaign but we did it so I'm gonna upload an actual final part for next week which will be a post-mortem on run force I'll talk about the various things that I learned during this project and why I think we were unsuccessful in dubbing Forrest as any course running master I think I have a good idea why the legit final and last part four is coming next week and I hope to see you there alright guys if you're enjoying this project and really want to help me out consider doing these things subscribe to my channel hit that Bell icon for upload notifications and leave a like on this video and share this video with a friend all that stuff helps out a lot more than you probably think and I thank you greatly for your support also I'm now taking suggestions so if there's any type of game or app or software that you want me to make leave your suggestions and comments below or even better tweet it to me I'm a bit of a Twitter at it and I'd love to talk to you on Twitter how many followers in America follow me but whatever the case may be remember to always feed your curiosity [Music]

Channel: Jabrils

Views: 439,999

Keywords: SEFD, San Diego, SEFD Sci, Curiosity, Education, Machine Learning, artificial intelligence, ai, deep learning, robots, deepmind, software engineering, computer science, software, computer programming, computers, online, super f(x), f(x), function, math, technology, research, data, solution, answer, questions, hard, software development, game, data science, how to, tutorial, demo, project, neural network, unity3D, video game, genetic algorithm, brain, robot, run forrest, forrest gump

Id: GDy45vT1xlA

Length: 14min 12sec (852 seconds)

Published: Sun Jan 14 2018

Reddit Comments

[u/iams3b]

Damn dude this is awesome even though I didn't understand any of it. Machine Learning is something i always wanted to poke into but I have no idea what it entails

I'ma check out the rest of your videos cheers

[u/KingPickle]

I enjoyed the video. Looking forward to pt4 :)