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Semi-model-Based Reinforcement Learning in Organic Computing Systems.

, , и . ARCS, том 13642 из Lecture Notes in Computer Science, стр. 241-255. Springer, (2022)

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Alice Pilar Pilchau

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Semi-model-Based Reinforcement Learning in Organic Computing Systems., , и . ARCS, том 13642 из Lecture Notes in Computer Science, стр. 241-255. Springer, (2022)Synthetic Experiences for Accelerating DQN Performance in Discrete Non-Deterministic Environments., , и . Algorithms, 14 (8): 226 (2021)Deep Q-Network Updates for the Full Action-Space Utilizing Synthetic Experiences., , , и . IJCNN, стр. 1-9. IEEE, (2023)Application of Uncertainty-Aware Sensor Fusion in Physical Sensor Networks., , , , , , , , , и 1 other автор(ы). I2MTC, стр. 1-6. IEEE, (2022)Averaging rewards as a first approach towards Interpolated Experience Replay.. GI-Jahrestagung (Workshops), том P-295 из LNI, стр. 493-506. GI, (2019)Interpolated Experience Replay for Continuous Environments., , и . IJCCI, стр. 237-248. SCITEPRESS, (2022)An Architectural Design for Measurement Uncertainty Evaluation in Cyber-Physical Systems., , , , , , , , , и 2 other автор(ы). FedCSIS (Position Papers), том 22 из Annals of Computer Science and Information Systems, стр. 53-57. (2020)Bootstrapping a DQN Replay Memory with Synthetic Experiences., , и . IJCCI, стр. 404-411. SCITEPRESS, (2020)