Building up Embodiment in Learning Agents Using A Gaussian Radial Basis Function Neural Network
Hajime Murao and Shinzo Kitamura
Department of Computer and Systems Engineering, Faculty of Engineering, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan
In this paper, we propose actor-critic learning with adaptive state space construction. The gaussian radial basis function neural network is employed for both the actor and the critic modules, where each hidden neuron covers a subspace of the sensor space and so the hidden layer corresponds to the state space. In the proposed algorithm, a robot starts without any states and a new state is generated incrementally by adding a new hidden neuron. One clear advantage of the proposed algorithm to others is the performance improvement by the minimal training after adding a new state, i.e. the adjustment of the connective strength between the new neuron and others is only required after adding a new hidden neuron. This provides an efficient method to construct the state space during the learning in the real world. Resulting state space represents aspects of the environment in which the robot works and the characteristic of the robot itself. In this sense, the obtained state space is said to represent the embodiment of the robot.
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