- Tinder Bot:
Tinderbox is an experiment built on the Tinder app API. Tinderbox is a full Tinder solution that learns who you're attracted to (using machine learning) and also has a built-in bot that can start conversations. It is a full desktop interface for Tinder.
Code:
https://github.com/crockpotveggies/tinderbox
- Candy Crush Bot:
A python bot that plays Candy Crush.
Due to lack of time when I coded this I warn you that it's full of hardcoded stuff :).
This is how it works:
Take a screenshot of the desktop
Extract the game board from it
From that game board extract each cell
Using a classification algorithm determine what candy is in each cell
Compute the best move using a greedy-like algorithm
Send Inputs to the browser window
Wait for the board to stabilize and all the movement to stop
Goto 1 :)
The main problem now is the fact that I hardcoded the position of the board in the screenshot. You need to ajust the offsets of it in order to match the ones for your browser and screen size.
Video:
https://www.youtube.com/watch?v=18vqQOPlvO4
Code:
https://github.com/AlexEne/CCrush-Bot
A python bot that plays Candy Crush.
Due to lack of time when I coded this I warn you that it's full of hardcoded stuff :).
This is how it works:
Take a screenshot of the desktop
Extract the game board from it
From that game board extract each cell
Using a classification algorithm determine what candy is in each cell
Compute the best move using a greedy-like algorithm
Send Inputs to the browser window
Wait for the board to stabilize and all the movement to stop
Goto 1 :)
The main problem now is the fact that I hardcoded the position of the board in the screenshot. You need to ajust the offsets of it in order to match the ones for your browser and screen size.
Video:
https://www.youtube.com/watch?v=18vqQOPlvO4
Code:
https://github.com/AlexEne/CCrush-Bot
- Atari 2600 Pong Reinforcement Learning, Pybrain.
A python reinforcement learning algorithms for the arcade learning environment.
Also requires the shared object python interface.
Video:
Atari 2600 Pong Reinforcement Learning demo.
Published on 10 Mar 2015 ·
This demo uses a tabular set of states that are extracted from ram (the state variables are displayed on screen), and trains using Q-learning, with the arcade learning environment. Research quality code that produced this is available at https://github.com/bbitmaster/pyrlcade
A python reinforcement learning algorithms for the arcade learning environment.
Also requires the shared object python interface.
Video:
Atari 2600 Pong Reinforcement Learning demo.
Published on 10 Mar 2015 ·
This demo uses a tabular set of states that are extracted from ram (the state variables are displayed on screen), and trains using Q-learning, with the arcade learning environment. Research quality code that produced this is available at https://github.com/bbitmaster/pyrlcade
Also, this demonstration uses a python arcade learning environment wrapper that can be used by anyone. It can be found at https://github.com/bbitmaster/ale_python_interface
This project was produced by Ben Goodrich as part of the Machine Intelligence Lab at the University of Tennessee
http://mil.engr.utk.edu/
https://youtu.be/PSQt5KGv7Vk
Code:
https://github.com/bbitmaster/pyrlcade
http://mil.engr.utk.edu/
https://youtu.be/PSQt5KGv7Vk
Code:
https://github.com/bbitmaster/pyrlcade
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