pong_ai

Brief Intro

This is a simple html5 game based on vue and tensorflow.js. It use vue for the control setting page, tensorflow.js to train the ai and indexdb to cache the data to train and trained ai.

Setting Explanation

You can change the game setting and model setting accordingly.

Here are some explanation for the setting.

1. Game Setting
   • Switch: pause or continue the game
   • Difficulty: this game has two levels to choose from. And the second level has an additional moving obstacle in the middle of the canvas.
   • Player Selected: player1 is the player below and vice versa. Each player can be controlled by user, rule based ai(just move to follow the ball around), and the netural network ai.
   • game speed: can be adjusted between 1 and 10, low speed is prefered for ai to have enough time to adjust
2. Model Setting
   • turns planned to train: how many games you planned to collect data to trian your ai
   • current turn: how many games you have played(will be reset after ai have been trained)
   • learn setting: determine which player you choose to learn from (actually is which player your train data is from)
   • clear system cache: click to clear all the train data has been collected since last train (note: not the data of in the indexDB just data cached in the memory)
   • use indexDB: For the data to train ai, it's always the more the better. You can use the data in the indexDB to add train data.(And the data will be automatically stored in the DB every time before the train, so you don't have to worry about the data source)
   • preparing to trian: If you don't plan to train your ai, you can uncheck this option.
   • Hit Rate: an assessment criteria for your ai

open the console for more details

AI Part

The model used for the ai is the simplest fully-connected network. And I don't have much time to improve its performance(currently just data balancing and drop out). You can adjust the model in /src/js/model/model.js. (And it's wired when I use softmax activation function for the output layer, the performace is particularly awful. I haven't find out why this happen.)

And according to my test, when the data volume accumulates to about 20000+, current ai starts to be a little smarter.

ref

1. The project is based on the tutorial provided by pythonprogramming.net.

2. tensorflow.js tutorial

3. Hello World with Tensorflow.js

4. indexDB api

5. indexDB tutorial 阮一峰

6. Another indexDB tutorial

7. IView api

8. 浅析js中的Promise和async/await

9. JS中的异步操作

10. import, export in es6

Project setup

npm install

Compiles and hot-reloads for development

npm run serve

Compiles and minifies for production

npm run build