Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract Cooperative decentralized multirobot learning refers to the use of multiple learning entities to learn optimal solutions for an overall multirobot system.  demonstrate that traditional single-robot learning theory can be successfully used with multirobot systems, but only under certain conditions. The success and the effectiveness of single-robot learning algorithms in multirobot systems aze potentially affected by various factors that  classify into two groups: the nature of the robots and the nature of the learning. Incorrect set-up of these factors may lead to undesirable results. ,  systematically test the effect of varying five common factors (model of the value function, rewazd scope, delay of global information, diversity of robots’ capabilities, and number of robots) in decentralized multirobot learning experiments, fast in simulation and then on real robots. The results show that three of these factors (model of the value function, reward scope, and delay of global inf...
['Poj Tangamchit', 'John M. Dolan', 'Pradeep K. Khosla']
I am writing a research paper on a idea Crucial Factors Affecting Cooperative Multirobot Learning
[{"from": "human", "value": "I am writing a research paper on a idea Crucial Factors Affecting Cooperative Multirobot Learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract Cooperative decentralized multirobot learning refers to the use of multiple learning entities to learn optimal solutions for an overall multirobot system.  demonstrate that traditional single-robot learning theory can be successfully used with multirobot systems, but only under certain conditions. The success and the effectiveness of single-robot learning algorithms in multirobot systems aze potentially affected by various factors that  classify into two groups: the nature of the robots and the nature of the learning. Incorrect set-up of these factors may lead to undesirable results. ,  systematically test the effect of varying five common factors (model of the value function, rewazd scope, delay of global information, diversity of robots\u2019 capabilities, and number of robots) in decentralized multirobot learning experiments, fast in simulation and then on real robots. The results show that three of these factors (model of the value function, reward scope, and delay of global inf..."}]