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Loss-Calibrated Monte Carlo Action Selection

Abbasnejad, Ehsan; Domke, Justin; Sanner, Scott


Bayesian decision-theory underpins robust decision-making 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...[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Conference paper
Source: HVAC-Aware Occupancy Scheduling


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