Flaippy Bird 101

Overview

• Flappy Bird is a wildly successful game released for iPhone in 2014. This project examines AI solutions to the game along the lines of reinforcement learning and informed search. For a detailed description of the project, read our white paper in this repository.

Getting started

• Make sure you've got PyGame 1.9+ installed and are running Python 2.7+. You may also need to update your PC's package of libpng.
• For a demo of the TD-lambda learner on pre-trained weights, run python flappy.py -d.
• Note that training can take a while (esp. as the agent gets better and each episodes lasts longer). Our best performing agent was trained for well over 6 hours.

RL Statespace

• 10 by 10 grid of 10 by 10 units immediately in front of the next gap.
• More than 50 above or below the gap, the y-discretization increases to 100.
• 100 horizontal units before the next gap the x-discretization increases to 100.
• Vertical velocity [-10, -9, ... , 9, 10].

TODOs

• Clean up command line args and general IO.
• pipes.py shouldn't exist.
• Make node_util.py not totally awful.
• Consolidate and clean up code. A lot is split between multiple files.
• Those global variables and constants though...
• The Q Learner commits suicide when prospects look grim. Work on getting it to be risky and aim for the gap even when failure is certain.