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Response knowledge learning of autonomous agent

In robot applications, the performance of a robot agent is measured by the award received from its response. A lot of literature defines the response as either a state diagram or a neural network. Due to the absence of a desired response, neither is applicable to an unstructured environment. In this paper, a novel response knowledge learning algorithm is proposed to handle this domain. By using a set of experiences, the algorithm can extract the contributed experiences to construct the response function. Two sets of environments are provided to illustrate the performance of the proposed algorithm. The results show that it can effectively construct a response function that receives an award which is very close to the true maximum.