Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study Bayesian optimal control of a general class of smoothly parameterized Markov decision problems. Since computing the optimal control is computationally expensive,  design an algorithm that trades off performance for computational efficiency. The algorithm is a lazy posterior sampling method that maintains a distribution over the unknown parameter. The algorithm changes its policy only when the variance of the distribution is reduced sufficiently. Importantly,  analyze the algorithm and show the precise nature of the performance vs. computation tradeoff. Finally,  show the effectiveness of the method on a b server control application.
['Yasin Abbasi-Yadkori', 'Csaba Szepesvári']
I am writing a research paper on a idea Bayesian Optimal Control of Smoothly Parameterized Systems: The Lazy Posterior Sampling Algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian Optimal Control of Smoothly Parameterized Systems: The Lazy Posterior Sampling Algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study Bayesian optimal control of a general class of smoothly parameterized Markov decision problems. Since computing the optimal control is computationally expensive,  design an algorithm that trades off performance for computational efficiency. The algorithm is a lazy posterior sampling method that maintains a distribution over the unknown parameter. The algorithm changes its policy only when the variance of the distribution is reduced sufficiently. Importantly,  analyze the algorithm and show the precise nature of the performance vs. computation tradeoff. Finally,  show the effectiveness of the method on a b server control application."}]