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We present a novel reinforcement learning based algorithm for multi-robottask allocation problem in warehouse environments. We formulate it as a MarkovDecision Process and solve via a novel deep multi-agent reinforcement learningmethod (called RTAW) with attention inspired policy architecture. Hence, ourproposed policy network uses global embeddings that are independent of thenumber of robots/tasks. We utilize proximal policy optimization algorithm fortraining and use a carefully designed reward to obtain a converged policy. Theconverged policy ensures cooperation among different robots to minimize totaltravel delay (TTD) which ultimately improves the makespan for a sufficientlylarge task-list. In our extensive experiments, we compare the performance ofour RTAW algorithm to state of the art methods such as myopic pickup distanceminimization (greedy) and regret based baselines on different navigationschemes. We show an improvement of upto 14% (25-1000 seconds) in TTD onscenarios with hundreds or thousands of tasks for different challengingwarehouse layouts and task generation schemes. We also demonstrate thescalability of our approach by showing performance with up to $1000$ robots insimulations.

RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments