  This paper investigates the dynamics of competition among organizations with
unequal expertise. Multi-agent reinforcement learning has been used to simulate
and understand the impact of various incentive schemes designed to offset such
inequality. We design Touch-Mark, a game based on well-known
multi-agent-particle-environment, where two teams (weak, strong) with unequal
but changing skill levels compete against each other. For training such a game,
we propose a novel controller assisted multi-agent reinforcement learning
algorithm \our\, which empowers each agent with an ensemble of policies along
with a supervised controller that by selectively partitioning the sample space,
triggers intelligent role division among the teammates. Using C-MADDPG as an
underlying framework, we propose an incentive scheme for the weak team such
that the final rewards of both teams become the same. We find that in spite of
the incentive, the final reward of the weak team falls short of the strong
team. On inspecting, we realize that an overall incentive scheme for the weak
team does not incentivize the weaker agents within that team to learn and
improve. To offset this, we now specially incentivize the weaker player to
learn and as a result, observe that the weak team beyond an initial phase
performs at par with the stronger team. The final goal of the paper has been to
formulate a dynamic incentive scheme that continuously balances the reward of
the two teams. This is achieved by devising an incentive scheme enriched with
an RL agent which takes minimum information from the environment.
