  Autonomous drone swarms are a burgeoning technology with significant
applications in the field of mapping, inspection, transportation and
monitoring. To complete a task, each drone has to accomplish a sub-goal within
the context of the overall task at hand and navigate through the environment by
avoiding collision with obstacles and with other agents in the environment. In
this work, we choose the task of optimal coverage of an environment with drone
swarms where the global knowledge of the goal states and its positions are
known but not of the obstacles. The drones have to choose the Points of
Interest (PoI) present in the environment to visit, along with the order to be
visited to ensure fast coverage. We model this task in a simulation and use an
agent-oriented approach to solve the problem. We evaluate different policy
networks trained with reinforcement learning algorithms based on their
effectiveness, i.e. time taken to map the area and efficiency, i.e.
computational requirements. We couple the task assignment with path planning in
an unique way for performing collision avoidance during navigation and compare
a grid-based global planning algorithm, i.e. Wavefront and a gradient-based
local planning algorithm, i.e. Potential Field. We also evaluate the Potential
Field planning algorithm with different cost functions, propose a method to
adaptively modify the velocity of the drone when using the Huber loss function
to perform collision avoidance and observe its effect on the trajectory of the
drones. We demonstrate our experiments in 2D and 3D simulations.
