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
If on Ubuntu you can install tkinter for python2.7 with $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)
##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
#Due Date is Thursday at noon PST January 12th 2017
##Credits
The credits for this code go to PhillipeMorere. I've merely created a wrapper to get people started.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent