Package: grid-royale
GridRoyaleView the live version here!GridRoyaleis a life simulation. It's a tool for machine learning researchers to explore social dynamics.It's similar toGame of LifeorGridWorld,
except I added game mechanics to encourage the players to behave socially. These game mechanics are
similar to those in thebattle royalegenre of
computer games, which is why it's called GridRoyale.The game mechanics, Python framework and visualization are pretty good-- The core algorithm sucks, andI'm waiting for someone better than me to come and write a new one.If that's you, please open a
pull request.Let's back up for a bitThis project is difficult to explain, and it's also one I've been dreaming of doing for years.
Please bear with me as I do my best to explain it in the paragraphs below. I'll also note that I'm a
newbie at machine learning and specifically reinforcement learning, so it's quite likely that there
is lots of relevant research I'm not familiar with.My goal is to explore social dynamics.Like most everyone, I spend a lot of time thinking why people do what they do. How they behave with
each other, how they communicate, and how they decide whether they're friends, enemies, or anything
murky in between. Sometimes people behave in ways that can be easily analyzed logically, and
sometimes they don't. Even when they behave in ways that seem unreasonable, I know it's mostly
because my reasoning isn't advanced enough.That's obviously a very deep rabbit hole to go into. My guiding principle is this: Social behaviors
that we see in the world go through a process that's kind of like evolution, meaning survival of the
fittest. There are lots of people in the world, they live for a long time and they get to try lots
of different behaviors.Behaviors that don't make some kind of sense, don't survive.If you see
someone behaving in some way, and that person made it safely to adulthood without drinking bleach
from the under-sink cabinet in the kitchen, then in some way that behavior is "the fittest" or close
to it, whatever "fittest" means.Whatever theory we have about why people do what they do, it all starts with trying to survive, and
then building layers of abstraction on top of that.Let's cut back to the technical side of the project: Reinforcement learning, game theory, neural
networks, and Canvas animations in the browser.My thinking is, maybe I can create a simulation where creatures move about in a virtual world. Maybe
I can set up the rules of that world to be as conducive as possible to social behaviors. Then I'd
see these creatures engage in social behaviors in ways that are totally unscripted, and motivated
only by their desire to survive. I could analyze what they do, why, what works and what doesn't. For
any behavior that seems unreasonable, I could perform an unlimited number of experiments to find
where it is useful, and what would happen if it were removed. I could go backward and forward in
time, create parallel realities, and basically have full freedom to run any kind of scenario.I'm hoping I could use that to model human behaviors in the real world.I could use neural networks to optimize the creatures' behaviors according to the rewards that they
get, use Python to manage the state of the world and make it easy for people to write different
policies for the creatures to use, and then display the simulation in a nice Canvas animation in
the browser.I started working on this project in June of 2020. This included giving myself a crash course in
neural networks (brag), reinforcement learning and JavaScript
animation. As I'm writing these lines (October 2020) I have to stop working on this project, because
I'm starting a new full-time job. I'll describe what I did and what I'd like to see, andI hope
that other people will be interested in continuing this research.What I did so farThe creatures are able to walk around, eat food that's spawned in random cells in the grid, bump
into each other and shoot each other. Eating a piece of food grants the creature 10 points. Bumping
into another creature costs 5 points. Getting shot costs 10 points.I used Keras to write a Q-learning algorithm with a neural network, and that got them to learn how
to walk towards the food and avoid bumping into each other. They're not smart enough yet to avoid
bullets or have a policy.There's a nice browser interface that lets you see the entire animation and skip to any timepoint
you'd like.What behaviors I'd like to seeHere'sa
studythat was done at Facebook AI Research. If you're too lazy to read the whole thing, they trained bots
to negotiate with each other for items they're trading, and the bots gradually learned how to be
shrewd and get better deals. They used a language to communicate between themselves and they learned
to give the words their own meaning.That research has good examples for things I want to see, and good examples for things that arethe
oppositeof what I'd like to see.Yes, I'd like to see creatures communicating with each other and working to become richer. What Idon'twant is to have these interactions be so scripted. I don't want the creatures to have
predefined tasks, and predefined sessions in which they communicate using predefined words. I want
all of their social behavior to be spontaneous and built on top of their basic survival routine. I
want them to live in an open world, in which they decide whether they'd like to communicate or not.
I don't want episodes of communication that have a clear start and end, I want communication to be a
part of the creatures' movement.This is getting pretty abstract, so let me give an example.I've already got creatures that know how to go for food and avoid bumping into other creatures.
That's a start. Now, imagine that creature X becomes smart enough to figure out that if they get
close to other creatures, these other creatures are going to walk away, because they'd like to avoid
confrontation. Creature X can therefore establish a territory, i.e. a block of space that no other
creatures would step into. This means that creature X could eat all the food that's randomly spawned
in its territory. It also needs to routinely patrol the edges of its territory so it could chase
away any intruders. This can get difficult if there are several intruders at a time.A territory is an interesting thing. It's more of a game theory concept than a physical concept.
Territories exist outside of physical space, like in intellectual property or internet domain names.
The basic condition that makes territories possible is that a player has enough incentive to be
stubborn and not give up on the property no matter what, and then can convince the other players of
how stubborn it is. It's a combination of cooperation and conflict, since both sides are fighting
over territory, but they also have an interest to end the war as quickly as possible. That's what
makes it so interesting to study.Say that the other creatures have also learned this territorial behavior. Each creature now has its
own territory, and that territory is bordered by its neighbors' territories. Some borders will be
more peaceful, and others will have fights over territory on them, resulting in one side getting a
bit of the other's territory.If you're creature X and you have a few neighbors, you can recognize that some of them get into more
fights with you than others. The neighbors that tend not to pick fights at the borders are now your
assets. You save so much time by not having to patrol that part of your territory. This means that
if one of these good neighbors get attacked, it will be in your interest to help them win. If
they'll be pushed aside by a more aggressive creature, that creature might invade your territory
later.Now we have communication and cooperation that are emergent phenomena. We can have complex
relationship between creatures, and hopefully something resembling a society.Another thing that's important to me is that I want things to be visual. I don't want people to have
to read a research paper to understand how the creatures are cooperating, I want them to see the
creatures doing that. That's why I chose to use Canvas animations in the browser, so people could
play with the results without having to install anything.How to run this codeInstallation:$pipinstallgrid_royaleRun the server:$grid_royaleplayThis will automatically open a browser window and show you your simulation.How to extend this codeThe creatures' policy is defined by thePolicyclassclass. Most of
its logic is defined by the more generalModelFreeLearningPolicyclass,
which defines a neural network and uses it to decide on actions. You can change the logic in any of
these classes and see what changes come out in the simulation. The main two methods that get called
by outside code aredecide_action_for_observationandtrain.I wrote the code to be nice and modular, so when you're writing your logic in thePolicyclass,
you don't need to think at all about maintaining the rules of the game or showing the animation. Thedecide_action_for_observationmethod gets an observation of the world and needs to return an
action, and all the rest is taken care of.Either clone the repo or runpip install -e grid_royale, then change the code. Run it usinggrid_royale playand see how your creatures fare.If you'd like to discuss your approach or need help, either open an issue or email me atram@rachum.com