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Reward-Reinforced Generative Adversarial Networks for Multi-Agent Systems.

, , , , and . IEEE Trans. Emerg. Top. Comput. Intell., 6 (3): 479-488 (2022)

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Class mutation operators for C++ object-oriented systems., , , , and . Ann. des Télécommunications, 70 (3-4): 137-148 (2015)Combining Evolutionary Mutation Testing with Random Selection., , and . CEC, page 1-8. IEEE, (2020)Towards automated provenance collection for runtime models to record system history., , and . SAM, page 12-21. ACM, (2020)Event-driven temporal models for explanations - ETeMoX: explaining reinforcement learning., , , , , , , and . Softw. Syst. Model., 21 (3): 1091-1113 (2022)Mutación evolutiva., , , and . JISBD, page 281-292. IBERGARCETA Pub. S.L., (2010)Model-driven Design of Performance Requirements with UML and MARTE., , and . ICSOFT (2), page 54-63. SciTePress, (2011)Cronista: A multi-database automated provenance collection system for runtime-models., , and . Inf. Softw. Technol., (2022)Computing performance requirements for web service compositions., , , , and . Comput. Stand. Interfaces, (2023)A Framework for History-Aware Hyperparameter Optimisation in Reinforcement Learning., , , , , , , and . CoRR, (2023)GiGAn: evolutionary mutation testing for C++ object-oriented systems., , , , and . SAC, page 1387-1392. ACM, (2017)