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A Unified Framework for Deep Symbolic Regression.

, , , , , , , , и . NeurIPS, (2022)

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Werner Glatt

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Ernst Glatt

 

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Improving exploration in policy gradient search: Application to symbolic optimization., , , , , , , и . CoRR, (2021)Deep Reinforcement Learning-Based Optimal Parameter Design of Power Converters., , , , , , , , и . ICNC, стр. 25-29. IEEE, (2023)Simultaneously Learning and Advising in Multiagent Reinforcement Learning., , и . AAMAS, стр. 1100-1108. ACM, (2017)Symbolic Regression via Neural-Guided Genetic Programming Population Seeding., , , , , и . CoRR, (2021)A Framework to Discover and Reuse Object-Oriented Options in Reinforcement Learning., , , , и . BRACIS, стр. 109-114. IEEE Computer Society, (2018)MOO-MDP: An Object-Oriented Representation for Cooperative Multiagent Reinforcement Learning., , и . IEEE Trans. Cybern., 49 (2): 567-579 (2019)Abmarl: Connecting Agent-Based Simulations with Multi-Agent Reinforcement Learning., и . J. Open Source Softw., 6 (64): 3424 (2021)A Review on Simulation Platforms for Agent-Based Modeling in Electrified Transportation., , , и . IEEE Trans. Intell. Transp. Syst., 25 (2): 1131-1147 (февраля 2024)Discovering symbolic policies with deep reinforcement learning., , , , , , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 5979-5989. PMLR, (2021)Policy Reuse in Deep Reinforcement Learning., и . AAAI, стр. 4929-4930. AAAI Press, (2017)