Ivan Seidel
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117 linhas
4.0 KiB
Markdown
117 linhas
4.0 KiB
Markdown
# IAMDinosaur
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A simple artificial intelligence to teach Google Chrome's offline dinosaur to
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jump cactus, using Neural Networks and a simple Genetic Algorithm.
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**Watch** this video to see it in action, and learn how it works: [Artificial Inteligence with Google's Dinosaur](https://youtu.be/P7XHzqZjXQs)
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## Installation
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1. Install `Node.js` on your computer.
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2. Clone/download this folder to your computer.
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3. run `npm install` within this folder
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4. Open Chrome's dinosaur game and put aside the terminal (It MUST be on the same screen)
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**(Tip: go to developer tools, and under network, set to offline )**
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5. run `node index` within this folder. If the game was located, it will move the cursor
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of the mouse to the origin of the `floor` of the dino. Press `s` key in the terminal to
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start learning.
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## How does it work
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We have 3 different inputs read from the pixels of the screen:
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1. Distance from the next cactus
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2. Length of the next cactus
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3. Speed of the current cactus
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We have also, one output with 3 possible states:
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1. output < 0.45: Press DOWN key
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2. output > 0.55: Press UP key
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2. default: Release both keys
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## Genetic Algorithm
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Each Generation consists of 12 neural networks (Genomes).
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Each genome is tested with the game, by constantly mapping the read
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inputs from the game to the inputs of the neural network, and by getting
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the output/activation from the network and applying to the keys of the
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keyboard.
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While testing each genome, we keep track of it's "fitness" by counting
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jumped cactus in the game.
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When an entire generation is completed, we remove the worst genomes until
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achieving `N` genomes. With those `N` genomes, we then select two randomly,
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and cross-over their values/configurations. After that, we apply random mutations
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in the values/configurations of the Neural Network, creating a new genome.
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We do the cross-over/mutation until we get 12 genomes again, and repeat it constantly.
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## Implementation
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All the implementation was done using Node.js, with Synaptic (Neural Network library),
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and RobotJs (a library to read pixels and simulate key presses).
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There are a few files in the project:
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- `index.js`: It tight all things together.
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- `Scanner.js`: Basic abstraction layer above RobotJs library that reads the screen like
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ray tracing. Also have some utilities functions.
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- `UI.js`: Global scope for the UI management. It initializes and also updates the screen
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on changes.
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- `GameManipulator.js`: Has all the necessary code to read sensors, and apply outputs
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to the game. Is also responsible for computing points, getting the game state and
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triggering callbacks/listeners to real implementation.
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- `Learner.js`: It is the core implementation of the Genetic Algorithm. This is where
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"magic" happens, by running generations, doing "natural" selection, cross-over, mutation...
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### How to: Load a genome
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1. Make sure Genome is inside `genomes` folder with a `.json` extension
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2. Run the program
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3. Click the list in the terminal
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4. Navigate up/down to the wanted file
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5. Press `enter` (then, to start, press `s`)
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### Be aware of a game bug
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The dino game has a anoying bug: It starts to "drift" to the right with time
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making the dino to be wrong offseted from the origin of the game. That, makes
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the program to read the dino as a cactus, since it is the same color.
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You can fix that by continuously refreshing the page, or, by pasting this code inside the
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console in the element inspector:
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```
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// Make sure the dino does not drift to the right
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setInterval(function (){Runner.instance_.tRex.xPos = 21}, 2000)
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```
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## Development guidelines
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Please, follow the Node.js style guide from [Felix](https://github.com/felixge/node-style-guide).
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It is not complex, and has a great simple pattern for things.
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## Credits
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- [Ivan Seidel](https://github.com/ivanseidel)
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- [João Pedro](https://github.com/joaopedrovbs)
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- [Tony Ngan](https://github.com/tngan) **The idea came from him**
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