  We consider a setting where a supervisor delegates an agent to perform a
certain control task, while the agent is incentivized to deviate from the given
policy to achieve its own goal. In this work, we synthesize the optimal
deceptive policies for an agent who attempts to hide its deviations from the
supervisor's policy. We study the deception problem in the continuous-state
discrete-time stochastic dynamics setting and, using motivations from
hypothesis testing theory, formulate a Kullback-Leibler control problem for the
synthesis of deceptive policies. This problem can be solved using backward
dynamic programming in principle, which suffers from the curse of
dimensionality. However, under the assumption of deterministic state dynamics,
we show that the optimal deceptive actions can be generated using path integral
control. This allows the agent to numerically compute the deceptive actions via
Monte Carlo simulations. Since Monte Carlo simulations can be efficiently
parallelized, our approach allows the agent to generate deceptive control
actions online. We show that the proposed simulation-driven control approach
asymptotically converges to the optimal control distribution.
