EFFICIENT, SWARM-BASED PATH FINDING IN UNKNOWN GRAPHS USING REINFORCEMENT LEARNING

This paper addresses the problem of steering a swarm of autonomous agents out of an unknown maze to some goal located at an unknown location.This is particularly the case in situations where no direct communication between the agents is possible and all information exchange between agents has to occur indirectly through information "deposited" in the environment.To address this task, an  -greedy, collaborative reinforcement learning method using only local information exchanges is introduced in this paper to balance exploitation and exploration in the unknown maze and to optimize the ability of the swarm to exit from the maze.The learning and routing algorithm given here provides a mechanism for storing data needed to represent the collaborative utility function based on the experiences of previous agents visiting a node that results in routing decisions that improve with time.Two theorems show the theoretical soundness of the proposed learning method and illustrate the importance of the stored information in improving decision-making for routing.Simulation examples show that the introduced simple rules of learning from past experience significantly improve performance over random search and search based on Ant Colony Optimization, a metaheuristic algorithm. I. INTRODUCTIONThis paper presents a randomized, distributed approach to steer a swarm of agents out of any type of unknown maze to a goal located at some unknown location using only locally stored information and no direct communication between the agents.This is an important problem not only for groups of autonomous robots but also for minimum overhead distributed routing and graph search problems for a wide range of applications.The approach presented here employs a collaborative reinforcement learning (RL) framework and is based on formal results underlining the soundness of the approach.The problem of robot learning to escape a maze is not new to the machine learning research community; it was originally posed many decades back by H. Abelson and A. A. diSessa in [1].Since then, there has been a great deal of research in robots learning to navigate in and escape from a maze.In [5] an architecture for autonomous mobile agents is proposed that maps a two-dimensional environment, and provides safe paths to unexplored regions.In [6], algorithms are proposed for two heterogeneous robots searching for each

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