Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The selection of leaders in leader-follor multi-agent systems can be naturally formulated as a matroid optimization problem. ,  investigate the online and stochastic version of such a problem, where in each iteration or round,  select a set of leaders and then observe a random realization of the corresponding reward, i.e., of the system performance. This problem is referred to as a stochastic matroid bandit, a variant of combinatorial multi-armed bandit problems where the underlying combinatorial structure is a matroid.  consider semi-bandit feedback and Bernoulli rewards, and derive a tight and problem-dependent lor bound on the regret of any consistent algorithm.  propose KL-OSM, a computationally efficient algorithm that exploits the matroid structure.  derive a finite-time upper bound of the regret of KL-OSM that improves the performance guarantees of existing algorithms. This upper bound actually matches our lor bound, i.e., KL-OSM is asymptotically optimal. Numerical experiments attest that KL-OSM outperforms state-of-the-art algorithms in practice, and the difference in some cases is significant.
['Mohammad Sadegh Talebi', 'Alexandre Proutiere']
I am writing a research paper on a idea An Optimal Algorithm for Stochastic Matroid Bandit Optimization
[{"from": "human", "value": "I am writing a research paper on a idea An Optimal Algorithm for Stochastic Matroid Bandit Optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The selection of leaders in leader-follor multi-agent systems can be naturally formulated as a matroid optimization problem. ,  investigate the online and stochastic version of such a problem, where in each iteration or round,  select a set of leaders and then observe a random realization of the corresponding reward, i.e., of the system performance. This problem is referred to as a stochastic matroid bandit, a variant of combinatorial multi-armed bandit problems where the underlying combinatorial structure is a matroid.  consider semi-bandit feedback and Bernoulli rewards, and derive a tight and problem-dependent lor bound on the regret of any consistent algorithm.  propose KL-OSM, a computationally efficient algorithm that exploits the matroid structure.  derive a finite-time upper bound of the regret of KL-OSM that improves the performance guarantees of existing algorithms. This upper bound actually matches our lor bound, i.e., KL-OSM is asymptotically optimal. Numerical experiments attest that KL-OSM outperforms state-of-the-art algorithms in practice, and the difference in some cases is significant."}]