Opponent Modeling in Deep Reinforcement Learning

@inproceedings{He2016OpponentMI,
  title={Opponent Modeling in Deep Reinforcement Learning},
  author={He He and Jordan L. Boyd-Graber and Kevin Kwok and Hal Daum{\'e}},
  booktitle={ICML},
  year={2016}
}
Opponent modeling is necessary in multi-agent settings where secondary agents with competing goals also adapt their strategies, yet it remains challenging because strategies interact with each other and change. Most previous work focuses on developing probabilistic models or parameterized strategies for specific applications. Inspired by the recent success of deep reinforcement learning, we present neural-based models that jointly learn a policy and the behavior of opponents. Instead of… CONTINUE READING
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