A planning modular neural-network robot for asynchronous multi-goal navigation tasks

This paper focuses on two planning neural-network controllers, a "forward planner" and a "bidirectional planner". These have been developed within the framework of Sutton\u27s Dyna-PI architectures (planning within reinforcement learning) and have already been presented in previous papers. The novelty of this paper is that the architecture of these planners is made modular in some of its components in order to deal with catastrophic interference. The controllers are tested through a simulated robot engaged in an asynchronous multi-goal path-planning problem that should exacerbate the interference problems. The results show that: (a) the modular planners can cope with multi-goal problems allowing generalisation but avoiding interference; (b) when dealing with multi-goal problems the planners keeps the advantages shown previously for one-goal problems vs. sheer reinforcement learning; (c) the superiority of the bidirectional planner vs. the forward planner is confirmed for the multi-goal task