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Actor Based Simulation for Closed Loop Control of Supply Chain using Reinforcement Learning., , , , , , и . AAMAS, стр. 1802-1804. International Foundation for Autonomous Agents and Multiagent Systems, (2019)Learning to Minimize Cost to Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce., , , , , , и . COMAD/CODS, стр. 176-184. ACM, (2023)Identifying efficient curricula for reinforcement learning in complex environments with a fixed computational budget., , и . COMAD/CODS, стр. 81-89. ACM, (2022)Sentiment Extraction from Consumer-Generated Noisy Short Texts., , и . ICDM Workshops, стр. 399-406. IEEE Computer Society, (2017)Reinforcement Learning of Supply Chain Control Policy Using Closed Loop Multi-agent Simulation., , , , , , и . MABS, том 12025 из Lecture Notes in Computer Science, стр. 26-38. Springer, (2019)Sample Efficient Training in Multi-Agent Adversarial Games with Limited Teammate Communication., и . CoRR, (2020)Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning., и . COMAD/CODS, стр. 108-112. ACM, (2023)Multi-Document Summarization Using Distributed Bag-of-Words Model., , , и . WI, стр. 672-675. IEEE Computer Society, (2018)TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture., и . SemEval@NAACL-HLT, стр. 291-299. Association for Computational Linguistics, (2018)Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets., , , и . WASSA@EMNLP, стр. 193-199. Association for Computational Linguistics, (2017)