Inverse Reinforcement Learning for Strategy Identification

@article{Rucker2021InverseRL,
  title={Inverse Reinforcement Learning for Strategy Identification},
  author={Mark Rucker and Stephen C. Adams and Roy Hayes and Peter A. Beling},
  journal={2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  year={2021},
  pages={3067-3074}
}
In adversarial environments, one side could gain an advantage by identifying the opponent’s strategy. For example, in combat games, if an opponent’s strategy is identified as overly aggressive, one could lay a trap that exploits the opponent’s aggressive nature. However, an opponent’s strategy is not always apparent and may need to be estimated from observations of their actions. This paper proposes to use inverse reinforcement learning (IRL) to identify strategies in adversarial environments… 

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