  In this paper, we consider the problem of path finding for a set of
homogeneous and autonomous agents navigating a previously unknown stochastic
environment. In our problem setting, each agent attempts to maximize a given
utility function while respecting safety properties. Our solution is based on
ideas from evolutionary game theory, namely replicating policies that perform
well and diminishing ones that do not. We do a comprehensive comparison with
related multiagent planning methods, and show that our technique beats state of
the art RL algorithms in minimizing path length by nearly 30% in large spaces.
We show that our algorithm is computationally faster than deep RL methods by at
least an order of magnitude. We also show that it scales better with an
increase in the number of agents as compared to other methods, path planning
methods in particular. Lastly, we empirically prove that the policies that we
learn are evolutionarily stable and thus impervious to invasion by any other
policy.
