  When dealing with a series of imminent issues, humans can naturally
concentrate on a subset of these concerning issues by prioritizing them
according to their contributions to motivational indices, e.g., the probability
of winning a game. This idea of concentration offers insights into
reinforcement learning of sophisticated Large-scale Multi-Agent Systems (LMAS)
participated by hundreds of agents. In such an LMAS, each agent receives a long
series of entity observations at each step, which can overwhelm existing
aggregation networks such as graph attention networks and cause inefficiency.
In this paper, we propose a concentration network called ConcNet. First,
ConcNet scores the observed entities considering several motivational indices,
e.g., expected survival time and state value of the agents, and then ranks,
prunes, and aggregates the encodings of observed entities to extract features.
Second, distinct from the well-known attention mechanism, ConcNet has a unique
motivational subnetwork to explicitly consider the motivational indices when
scoring the observed entities. Furthermore, we present a concentration policy
gradient architecture that can learn effective policies in LMAS from scratch.
Extensive experiments demonstrate that the presented architecture has excellent
scalability and flexibility, and significantly outperforms existing methods on
LMAS benchmarks.
