• Corpus ID: 17845195

Designing Deception in Adversarial Reinforcement Learning

@inproceedings{Choudhury2011DesigningDI,
  title={Designing Deception in Adversarial Reinforcement Learning},
  author={Sanjiban Choudhury and Alok Kanti Deb and Jayanta Mukherjee},
  year={2011}
}
In an adversarial scenario, deceptions are powerful tools capable of earning time delayed rewards which an agent can use to circumvent the opponent’s counter attack. This paper illustrates deception as a complementary policy to direct objective satisfaction. In this paper, a framework for deceptions is defined to finally determine the number and nature of these actions. A minimal set of these actions ensures fast learning while being robust enough to confront any strong opponent. To satisfy the… 

Deceptive robot motion: synthesis, analysis and experiments

An analysis of deceptive motion is presented, starting with how humans would deceive, moving to a mathematical model that enables the robot to autonomously generate deceptive motion, and ending with studies on the implications of deceive motion for human-robot interactions and the effects of iterated deception.

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