AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

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Fast-Tracking Stationary MOMDPs for Adaptive Management Problems
Martin Péron, Kai Helge Becker, Peter Bartlett, Iadine Chadès

Last modified: 2017-02-12

Abstract


Adaptive management is applied in conservation and natural resource management, and consists of making sequential decisions when the transition matrix is uncertain. Informally described as ’learning by doing’, this approach aims to trade off between decisions that help achieve the objective and decisions that will yield a better knowledge of the true transition matrix. When the true transition matrix is assumed to be an element of a finite set of possible matrices, solving a mixed observability Markov decision process (MOMDP) leads to an optimal trade-off but is very computationally demanding. Under the assumption (common in adaptive management) that the true transition matrix is stationary, we propose a polynomial-time algorithm to find a lower bound of the value function. In the corners of the domain of the value function (belief space), this lower bound is provably equal to the optimal value function. We also show that under further assumptions, it is a linear approximation of the optimal value function in a neighborhood around the corners. We evaluate the benefits of our approach by using it to initialize the solvers MO-SARSOP and Perseus on a novel computational sustainability problem and a recent adaptive management data challenge. Our approach leads to an improved initial value function and translates into significant computational gains for both solvers.

Keywords


Partially observable Markov decision process; mixed observable Markov decision process; adaptive management; adaptive learning policy; exploration/exploitation trade-off

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