Q Learning Demo

This is the code for "How to use Q Learning in Video Games Easily" by Siraj Raval on Youtube

##Overview

This is the associated code for this video on Youtube by Siraj Raval. This is a simple example of a type of reinforcement learning called Q learning.

● 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

##Usage

Run python Learner.py in terminal to see the the bot in action.

##Challenge

The challenge for this video is to

• modify the the game world so that it's bigger
• add more obstacles
• have the bot start in a different position

Bonus points if you modify the bot in some way that makes it more efficient

##Solution

My solution features the following

• random_start() start anywhere on the board.
• difficulty parameter that scales up the number of walls.
• create_reds(),create_greens() and create_walls() that add more special squares to help and hinder the agent.
• (x,y) scaling for larger boards
• max_q() changes for more randomized/Q-sensitive agent decision making.
• Tons more documentation (for my own learning)

##Credits

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