Finding optimal memoryless policies of POMDPs under the expected average reward criterion

DOI10.1016/j.ejor.2010.12.014zbMath1237.90250OpenAlexW2055418958MaRDI QIDQ418072

Baoqun Yin, Hong-Sheng Xi, Yan-Jie Li

Publication date: 14 May 2012

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2010.12.014

zbMATH Keywords

correlated actionsmemoryless policyperformance differencepolicy iteration with step sizesPOMDPs

Mathematics Subject Classification ID

Markov and semi-Markov decision processes (90C40)

Related Items (2)

Future memories are not needed for large classes of POMDPs ⋮ Unnamed Item

Uses Software

• POMDP

Cites Work

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• Basic ideas for event-based optimization of Markov systems
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• A survey of algorithmic methods for partially observed Markov decision processes
• Perturbation realization, potentials, and sensitivity analysis of Markov processes
• The Optimal Control of Partially Observable Markov Processes over a Finite Horizon
• CONVERGENCE OF SIMULATION-BASED POLICY ITERATION
• Simulation-based optimization of Markov reward processes
• The $n$th-Order Bias Optimality for Multichain Markov Decision Processes
• Event-Based Optimization of Markov Systems
• Potential-Based Online Policy Iteration Algorithms for Markov Decision Processes
• Performance optimization algorithms based on potentials for semi-Markov control processes

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