Entropy-Guided Control Improvisation

Marcell Vazquez-Chanlatte, Sebastian Junges, Daniel J. Fremont, and Sanjit Seshia. Entropy-Guided Control Improvisation. In Robotics: Science and Systems XVII (RSS), 2021.

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Abstract

High level declarative constraints provide a powerful (and popular) way to define and construct control policies; however, most synthesis algorithms do not support specifying the degree of randomness (unpredictability) of the resulting controller. In many contexts, e.g., patrolling, testing, behavior prediction, and planning on idealized models, predictable or biased controllers are undesirable. To address these concerns, we introduce the Entropic Reactive Control Improvisation (ERCI) framework and algorithm which supports synthesizing control policies for stochastic games that are declaratively specified by (i) a hard constraint specifying what must occur, (ii) a soft constraint specifying what typically occurs, and (iii) a randomization constraint specifying the unpredictability and variety of the controller, as quantified using causal entropy. This framework, which extends the state of the art by supporting arbitrary combinations of adversarial and probabilistic uncertainty in the environment. ERCI enables a flexible modeling formalism which we argue, theoretically and empirically, remains tractable.

BibTeX

@inproceedings{vazquez-rss21,
  author    = {Marcell Vazquez{-}Chanlatte and
               Sebastian Junges and
               Daniel J. Fremont and
               Sanjit Seshia},
  title     = {Entropy-Guided Control Improvisation},
  booktitle = {Robotics: Science and Systems XVII (RSS)},
  year      = {2021},
  abstract  = {High level declarative constraints provide a powerful  
(and popular) way to define and construct control policies;  
however, most synthesis algorithms do not support specifying  
the degree of randomness (unpredictability) of the resulting  
controller. In many contexts, e.g., patrolling, testing, behavior  
prediction, and planning on idealized models, predictable or  
biased controllers are undesirable. To address these concerns,  
we introduce the Entropic Reactive Control Improvisation (ERCI)  
framework and algorithm which supports synthesizing control  
policies for stochastic games that are declaratively specified by (i)  
a hard constraint specifying what must occur, (ii) a soft constraint  
specifying what typically occurs, and (iii) a randomization  
constraint specifying the unpredictability and variety of the  
controller, as quantified using causal entropy. This framework,  
which extends the state of the art by supporting arbitrary  
combinations of adversarial and probabilistic uncertainty in the  
environment. ERCI enables a flexible modeling formalism which  
we argue, theoretically and empirically, remains tractable.},
}