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This paper studies a number of variations on the topic of training a deep network using data generated by a Monte-Carlo Tree Search (MCTS) agent. The paper focuses on the Atari 2600 platform and is motivated by the observation that, while MCTS performs extremely well on Atari 2600 games, it is also too computationally expensive to be used in a realistic setting. The authors provide empirical results on a number of Atari 2600 games.