This paper proposes five partially recurrent neural networks architectures to evaluate the different roles played by interlayer and intralayer feedback connections in planning a temporal sequence of states. The first model has only one-to-one feedback connections from the output towards the input layer. This topology is taken as the reference one. The other models have interlayer and/or intralayer all-to-all feedback connections added to them. All feedback connections, but the one-to-one feedback links, are trainable. The models yield a sequence which take four blocks from an initial to a goal state, when these states are presented to the network. The models showed good performance for planning in different levels of complexity. The results suggest that the models have poor generalization power.
Planning; partially recurrent neural networks; intralayer feedback connection; interlayer feedback connection
