Multi Agent Path Finding using Evolutionary Game Theory
This addresses multi-agent path planning for autonomous systems, offering significant performance improvements, though it is incremental as it builds on existing evolutionary game theory concepts.
The paper tackles the problem of path finding for multiple agents in unknown stochastic environments by using evolutionary game theory to optimize utility and safety, achieving a 30% reduction in path length and at least an order of magnitude faster computation compared to state-of-the-art RL methods.
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.