Homeostatic Neural Network for Adaptive Control: Examination and Comparison

摘要

Functioning of the biologically inspired neural network with cellular homeostasis is studied in the paper. The network is applied to the task of the control of agent behavior in the stochastic multi-goal environment. Importance of different aspects of the approach is studied on the setups with partially disabled features of the model. It is shown that only full model, incorporating both cellular homeostasis and homeostatically dependent weight correction rule led to the emergence of adaptive behavior. The proposed model is also compared to the Q(λ) reinforcement learning on the same task with multiple goals. Results, illustrating the comparison between Q(λ) and homeostatic neural network, show that proposed approach outperforms conventional in terms of adaptivity, quality of control and convergence speed.