Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The sensitivity-based optimization of Markov systems has become an increasingly important area. From the perspective of performance sensitivity analysis, policy-iteration algorithms and gradient estimation methods can be directly obtained for Markov decision processes (MDPs). In this correspondence, the sensitivity-based optimization is extended to average reward partially observable MDPs (POMDPs).  derive the performance-difference and performance-derivative formulas of POMDPs. On the basis of the performance-derivative formula,  present a new method to estimate the performance gradients. From the performance-difference formula,  obtain a sufficient optimality condition without the discounted reward formulation.  also propose a policy-iteration algorithm to obtain a nearly optimal finite-state-controller policy.
['Yanjie Li', 'Baoqun Yin', 'Hongsheng Xi']
I am writing a research paper on a idea Partially Observable Markov Decision Processes and Performance Sensitivity Analysis
[{"from": "human", "value": "I am writing a research paper on a idea Partially Observable Markov Decision Processes and Performance Sensitivity Analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The sensitivity-based optimization of Markov systems has become an increasingly important area. From the perspective of performance sensitivity analysis, policy-iteration algorithms and gradient estimation methods can be directly obtained for Markov decision processes (MDPs). In this correspondence, the sensitivity-based optimization is extended to average reward partially observable MDPs (POMDPs).  derive the performance-difference and performance-derivative formulas of POMDPs. On the basis of the performance-derivative formula,  present a new method to estimate the performance gradients. From the performance-difference formula,  obtain a sufficient optimality condition without the discounted reward formulation.  also propose a policy-iteration algorithm to obtain a nearly optimal finite-state-controller policy."}]