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We present a novel reinforcement learning (RL) based task allocation anddecentralized navigation algorithm for mobile robots in warehouse environments.Our approach is designed for scenarios in which multiple robots are used toperform various pick up and delivery tasks. We consider the problem of jointdecentralized task allocation and navigation and present a two level approachto solve it. At the higher level, we solve the task allocation by formulatingit in terms of Markov Decision Processes and choosing the appropriate rewardsto minimize the Total Travel Delay (TTD). At the lower level, we use adecentralized navigation scheme based on ORCA that enables each robot toperform these tasks in an independent manner, and avoid collisions with otherrobots and dynamic obstacles. We combine these lower and upper levels bydefining rewards for the higher level as the feedback from the lower levelnavigation algorithm. We perform extensive evaluation in complex warehouselayouts with large number of agents and highlight the benefits overstate-of-the-art algorithms based on myopic pickup distance minimization andregret-based task selection. We observe improvement up to 14% in terms of taskcompletion time and up-to 40% improvement in terms of computing collision-freetrajectories for the robots.

DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments