  In this paper, we consider a territory guarding game involving pursuers,
evaders and a target in an environment that contains obstacles. The goal of the
evaders is to capture the target, while that of the pursuers is to capture the
evaders before they reach the target. All the agents have limited sensing range
and can only detect each other when they are in their observation space. We
focus on the challenge of effective cooperation between agents of a team.
Finding exact solutions for such multi-agent systems is difficult because of
the inherent complexity. We present Multi-Agent Pursuer-Evader Learning
(MAPEL), a class of algorithms that use spatio-temporal graph representation to
learn structured cooperation. The key concept is that the learning takes place
in a decentralized manner and agents use situation report updates to learn
about the whole environment from each others' partial observations. We use
Recurrent Neural Networks (RNNs) to parameterize the spatio-temporal graph. An
agent in MAPEL only updates all the other agents if an opponent or the target
is inside its observation space by using situation report. We present two
methods for cooperation via situation report update: a) Peer-to-Peer Situation
Report (P2PSR) and b) Ring Situation Report (RSR). We present a detailed
analysis of how these two cooperation methods perform when the number of agents
in the game are increased. We provide empirical results to show how agents
cooperate under these two methods.
