  Learning when to communicate and doing that effectively is essential in
multi-agent tasks. Recent works show that continuous communication allows
efficient training with back-propagation in multi-agent scenarios, but have
been restricted to fully-cooperative tasks. In this paper, we present
Individualized Controlled Continuous Communication Model (IC3Net) which has
better training efficiency than simple continuous communication model, and can
be applied to semi-cooperative and competitive settings along with the
cooperative settings. IC3Net controls continuous communication with a gating
mechanism and uses individualized rewards foreach agent to gain better
performance and scalability while fixing credit assignment issues. Using
variety of tasks including StarCraft BroodWars explore and combat scenarios, we
show that our network yields improved performance and convergence rates than
the baselines as the scale increases. Our results convey that IC3Net agents
learn when to communicate based on the scenario and profitability.
