  Neural networks allow Q-learning reinforcement learning agents such as deep
Q-networks (DQN) to approximate complex mappings from state spaces to value
functions. However, this also brings drawbacks when compared to other function
approximators such as tile coding or their generalisations, radial basis
functions (RBF) because they introduce instability due to the side effect of
globalised updates present in neural networks. This instability does not even
vanish in neural networks that do not have any hidden layers. In this paper, we
show that simple modifications to the structure of the neural network can
improve stability of DQN learning when a multi-layer perceptron is used for
function approximation.
