Reinforcement learning in PyGame By Daniel K Slater 

What is reinforcement learning? ● Agents are run within an environment. ● As they take actions they receive feedback ● They aim to maximize good feedback and minimize bad feedback ● Computer games are a great way to train reinforcement learning agents. we know we can learn games from just a sequence of images, so computer agents should be able to do the same thing (given enough computational power, time and the right algorithms). 

Deep learning in reinforcement learning ● Google deepmind recently published a set of papers which have the best performance for reinforcement learning on a number of atari games such as Pong ● They use a deep convolutional architecture to turn a 192x160 image into a much smaller representation of the state of the game A technique called Q-learning is then used to learn the best actions from those states 

● http://www.arcadelearningenvironment.org/ ● Framework for running agents against atari 2600 games ● Lots of atari roms now supported ● Used in many top research papers on reinforcement learning Arcade learning environment (ALE) 

PyGame ● http://pygame.org/ ● Most popular python games framework ● 1000’s of games, all free, all open source ● All written in Python 

Everything being in python is great ● Its easy to make modifications to the game, such as removing a menu screen We can grab values from within the game to use for reinforcement ○ In Pong we can get reinforcement from the scores ○ In Tetris we can get +ve reinforcement by intercepting the removeCompleteLines function ○ In a platform game we can grab the player's x position to encourage exploration 

PyGamePlayer ● https://github.com/DanielSlater/PyGamePlayer ● Allows running of PyGame games with zero touches ● Handles intercepting screen buffer and key presses ● Fixes the game frame rates 

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