摘要
In order to solve the combinative explosion problems in a continuous and high dimensional statespace,a function approximation approach is usually used to represent the state space.The normalized ra-dial basis function(NRBF)was adopted as the local function approximator and a kind of adaptive statespace construction strategy based on the NRBF(ASC-NRBF)was proposed,which enables the system toallocate appropriate number and size of the basis functions automatically.Combined with the reinforce-ment learning method,the proposed ASC-NRBF method was applied to the robot navigation problem.Simulation results illustrate the performance of the proposed method.
In order to solve the combinative explosion problems in a continuous and high dimensional state space, a function approximation approach is usually used to represent the state space. The normalized radial basis function （NRBF） was adopted as the local function approximator and a kind of adaptive state space construction strategy based on the NRBF （ASC-NRBF） was proposed, which enables the system to allocate appropriate number and size of the basis functions automatically. Combined with the reinforce- ment learning method, the proposed ASC-NRBF method was applied to the robot navigation problem. Simulation results illustrate the performance of the proposed method.
基金
the National Natural Science Foundation of China(No50305021)
