State of the Art Control of Atari Games Using Shallow Reinforcement Learning

@inproceedings{Liang2016StateOT,
  title={State of the Art Control of Atari Games Using Shallow Reinforcement Learning},
  author={Yitao Liang and Marlos C. Machado and Erik Talvitie and Michael H. Bowling},
  booktitle={AAMAS},
  year={2016}
}
The recently introduced Deep Q-Networks (DQN) algorithm has gained attention as one of the first successful combinations of deep neural networks and reinforcement learning. Its promise was demonstrated in the Arcade Learning Environment (ALE), a challenging framework composed of dozens of Atari 2600 games used to evaluate general competency in AI. It achieved dramatically better results than earlier approaches, showing that its ability to learn good representations is quite robust and general… CONTINUE READING

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