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Deception in A Multi-agent Adversarial Game: The Game of Guarding Several Territories.

, , and . SSCI, page 1321-1327. IEEE, (2020)

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Deception in A Multi-agent Adversarial Game: The Game of Guarding Several Territories., , and . SSCI, page 1321-1327. IEEE, (2020)Multi-objective fuzzy Q-learning to solve continuous state-action problems., , and . Neurocomputing, (2023)Online Optimization for Network Resource Allocation and Comparison with Reinforcement Learning Techniques., , , , and . CoRR, (2023)Multi-objective Lyapunov-based controller design for nonlinear systems via genetic programming., , , , and . Neural Comput. Appl., 34 (2): 1345-1357 (2022)Hierarchical Reinforcement Learning for Non-Stationary Environments., , , and . SSCI, page 1421-1428. IEEE, (2023)Learning Deception Using Fuzzy Multi-Level Reinforcement Learning in a Multi-Defender One-Invader Differential Game., , and . Int. J. Fuzzy Syst., 24 (7): 3015-3038 (2022)Hierarchical Reinforcement Learning With Multi Discount Factors In A Differential Game., , and . SSCI, page 686-693. IEEE, (2022)Multi-Invader Multi-Defender Differential Game Using Reinforcement Learning., , and . FUZZ-IEEE, page 1-8. IEEE, (2022)Deception In The Game of Guarding Multiple Territories: A Machine Learning Approach., , and . SMC, page 381-388. IEEE, (2020)