4 papers
Accelerated Online Risk-Averse Policy Evaluation in POMDPs with Theoretical Guarantees and Novel CVaR Bounds
Yaacov Pariente, Vadim Indelman
Risk-averse decision-making under uncertainty in partially observable domains is a central challenge in artificial intelligence and is essential for developing reliable autonomous…
POMDPPlanners: Open-Source Package for POMDP Planning
Yaacov Pariente, Vadim Indelman
We present POMDPPlanners, an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process (POMDP) planning algorithms. The package integrates…
Online Risk-Averse Planning in POMDPs Using Iterated CVaR Value Function
Yaacov Pariente, Vadim Indelman
We study risk-sensitive planning under partial observability using the dynamic risk measure Iterated Conditional Value-at-Risk (ICVaR). A policy evaluation algorithm for ICVaR is d…
Bounding Conditional Value-at-Risk via Auxiliary Distributions with Bounded Discrepancies
Yaacov Pariente, Vadim Indelman
In this paper, we develop a theoretical framework for bounding the CVaR of a random variable using another related random variable , under assumptions on their cumulative an…