Path Generation with LSTM Recurrent Neural Networks in the Context of the Multi-Agent Patrolling

We propose a conceptually simple new decentralised and non-communicating strategy for the multi-agent patrolling based on the LSTM architecture. The recurrent neural networks and more specifically the LSTM architecture, as machines to learn temporal series, are well adapted to the multi-agent patrol problem to the extent that they can be viewed as a decision problem over the time. For a given scenario, a LSTM neural network is first trained from data generated in simulation for that configuration, then embedded in agents that shall use it to navigate through the area to patrol choosing the next place to visit by feeding it with their current node. Finally, this new LSTM-based strategy is evaluated in simulation and compared with two representative strategies, a cognitive and centralised one, and a reactive and decentralised one. Preliminary results indicate that the proposed strategy is globally not better than the representative strategies for the aggregating criterion of average idleness, but better than the decentralised representative for the evaluation criteria of mean interval and quadratic mean interval.