  Autonomous marine environmental monitoring problem traditionally encompasses
an area coverage problem which can only be effectively carried out by a
multi-robot system. In this paper, we focus on robotic swarms that are
typically operated and controlled by means of simple swarming behaviors
obtained from a subtle, yet ad hoc combination of bio-inspired strategies. We
propose a novel and structured approach for area coverage using multi-agent
reinforcement learning (MARL) which effectively deals with the non-stationarity
of environmental features. Specifically, we propose two dynamic area coverage
approaches: (1) swarm-based MARL, and (2) coverage-range-based MARL. The former
is trained using the multi-agent deep deterministic policy gradient (MADDPG)
approach whereas, a modified version of MADDPG is introduced for the latter
with a reward function that intrinsically leads to a collective behavior. Both
methods are tested and validated with different geometric shaped regions with
equal surface area (square vs. rectangle) yielding acceptable area coverage,
and benefiting from the structured learning in non-stationary environments.
Both approaches are advantageous compared to a na\"{i}ve swarming method.
However, coverage-range-based MARL outperforms the swarm-based MARL with
stronger convergence features in learning criteria and higher spreading of
agents for area coverage.
