Pavlov’s Arcade Reinforcement Learning for a Breakout AI
نویسندگان
چکیده
In this paper we describe the design and implementation of a bot to play the arcade game Breakout. Instead of manually writing the bot, we use reinforcement learning techniques to learn a strategy by repeated sessions of Breakout where the learner is rewarded for advancing through the game and punished for losing. We show that our bot’s game performance increases with repeated plays, though it does not converge to a strategy that never loses.
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