Loss-Calibrated Monte Carlo Action Selection

5 years 8 months ago
Loss-Calibrated Monte Carlo Action Selection
Bayesian decision-theory underpins robust decisionmaking in applications ranging from plant control to robotics where hedging action selection against state uncertainty is critical for minimizing low probability but potentially catastrophic outcomes (e.g, uncontrollable plant conditions or robots falling into stairwells). Unfortunately, belief state distributions in such settings are often complex and/or high dimensional, thus prohibiting the efficient application of analytical techniques for expected utility computation when real-time control is required. This leaves Monte Carlo evaluation as one of the few viable (and hence frequently used) techniques for online action selection. However, loss-insensitive Monte Carlo methods may require large numbers of samples to identify optimal actions with high certainty since they may sample from high probability regions that do not disambiguate action utilities. In this paper we remedy this problem by deriving an optimal proposal distribution...
Ehsan Abbasnejad, Justin Domke, Scott Sanner
Added 27 Mar 2016
Updated 27 Mar 2016
Type Journal
Year 2015
Where AAAI
Authors Ehsan Abbasnejad, Justin Domke, Scott Sanner
Comments (0)