quantum concurrent actions selection

Reinforcement learning has become an important approach in machine learning . because of the good performance of online adaptation and the ability to powefully learn complex nonlinear systems,articial intelligence,especially robotics,is widely used . we solve some reinforcement learning problems using the quantum theory which results in increasing the speed of learning ,as well as keeping the balance between exploration and exploitation. in this work we investigate quantum computation opportunity for concurrent decision making and coordination in systems that can simultaneously execute multiple actions to perform tasks more efficiently.