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Accepted paper at IJCAI 2020

Semi-Markov Reinforcement Learning for Stochastic Resource Collection




Sebastian Schmoll, Matthias Schubert


29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020),
January 2020, Japan


We show that the task of collecting stochastic, spatially distributed resources (Stochastic Resource Collection, SRC) may be considered as a Semi-Markov-Decision-Process. Our Deep-Q-Network (DQN) based approach uses a novel scalable and transferable artificial neural network architecture. The concrete use-case of the SRC is an officer (single agent) trying to maximize the amount of fined parking violations in his area. We evaluate our approach on a environment based on the real-world parking data of the city of Melbourne. In small, hence simple, settings with short distances between resources and few simultaneous violations, our approach is comparable to previous work. When the size of the network grows (and hence the amount of resources) our solution significantly outperforms preceding methods. Moreover, applying a trained agent to a non-overlapping new area outperforms existing approaches. more