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However, different automation methods may give varying results that may not be optimal. Proper resource allocation is crucial as it may lead to significant cost reductions or increased effectiveness that results in increased revenues.<\/jats:p><jats:p>In this work, we first propose a novel representation that allows the modeling of a multi-process environment with different process-based rewards. These processes can share resources that differ in their eligibility. Then, we use double deep reinforcement learning to look for an optimal resource allocation policy. We compare those results with two popular strategies that are widely used in the industry. Learning optimal policy through reinforcement learning requires frequent interactions with the environment, so we also designed and developed a simulation engine that can mimic real-world processes.<\/jats:p><jats:p>The results obtained are promising. Deep reinforcement learning based resource allocation achieved significantly better results compared to two commonly used techniques.<\/jats:p>","DOI":"10.1007\/978-3-031-27815-0_13","type":"book-chapter","created":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T10:03:04Z","timestamp":1679738584000},"page":"177-189","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep Reinforcement Learning for\u00a0Resource Allocation in\u00a0Business Processes"],"prefix":"10.1007","author":[{"given":"Kamil","family":"\u017bbikowski","sequence":"first","affiliation":[]},{"given":"Micha\u0142","family":"Ostapowicz","sequence":"additional","affiliation":[]},{"given":"Piotr","family":"Gawrysiak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,26]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-662-49851-4_1","volume-title":"Process Mining","author":"W Aalst","year":"2016","unstructured":"Aalst, W.: Data science in action. 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