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This repository contains the code which can help us to understand how q-learning algorithm can be applied to build simple video game bot.

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jalajthanaki/Q_learning_for_simple_atari_game

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Q learning for simple atari game

This is a simple example of a type of reinforcement learning called Q learning.

Overview

We are building simple game and using Q-learning algorithm we built the bot which can able to will this simple game

● Rules: The agent (yellow box) has to reach one of the goals to end the game 
         (green or red cell).

● Rewards: Each step gives a negative reward of -0.04. 
           The red cell gives a negative reward of -1. 
           The green one gives a positive reward of +1.
           
● States: Each cell is a state the agent can be.
● Actions: There are only 4 actions. Up, Down, Right, Left.

Dependencies

  • Python 2.7
  • tkinter

Installation

  • To install tkinter You need to execute this command: $ sudo apt-get install python-tk

Usage

Run python Learner.py in terminal to see the the bot in action. It'll find the optimal strategy pretty fast (like in 15 seconds)

Credits

The credits for this code go to PhillipeMorere and joongwha. I've merely created a wrapper to get people started.

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This repository contains the code which can help us to understand how q-learning algorithm can be applied to build simple video game bot.

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