Active collaboration in relative observation for Multi-agent visual SLAM
  based on Deep Q Network
  This paper proposes a unique active relative localization mechanism for
multi-agent Simultaneous Localization and Mapping(SLAM),in which a agent to be
observed are considered as a task, which is performed by others assisting that
agent by relative observation. A task allocation algorithm based on deep
reinforcement learning are proposed for this mechanism. Each agent can choose
whether to localize other agents or to continue independent SLAM on it own
initiative. By this way, the process of each agent SLAM will be interacted by
the collaboration. Firstly, based on the characteristics of ORBSLAM, a unique
observation function which models the whole MAS is obtained. Secondly, a novel
type of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondence
between Q Value and state-action pair,abstract representation of agents in MAS
are learned in the process of collaboration among agents. Finally, each agent
must act with a certain degree of freedom according to MAS-DQN. The simulation
results of comparative experiments prove that this mechanism improves the
efficiency of cooperation in the process of multi-agent SLAM.
