Deep Conditional Multi Task Learning in Atari
Deep Q Networks (DQN) have become one of the most successful architectures for the domain of Atari as it has been shown to outperform humans on most games. The problem at hand is that the training time for DQN is immense and the network must be retrained for each game. Through conditional neural networks (Condnets) we break our network up into blocks that can each specialize in solving a particular sub problem of a particular game. We then look one level further and consider approaching the problem of learning a shared representation across multiple games.