Playing Atari on RAM with Deep Q-learning

In 2013 the Deepmind team invented an algorithm called deep Q-learning. It learns to play Atari 2600 games using only the input from the screen. Following a call by OpenAI, we adapted this method to deal with a situation where the playing agent is given not the screen, but rather the RAM state of the Atari machine. Our work was accepted to the Computer Games Workshop accompanying the IJCAI 2016 conference. This post describes the original DQN method and the changes we made to it. You can re-create our experiments using a publicly available code.

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