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Multi-robot Cooperative Box-pushing problem using Multi-objective Particle Swarm Optimization Technique

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Max-affine regression with universal parameter estimation for small-ball designs., , , and . ISIT, page 2706-2710. IEEE, (2020)Breaking the $T$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits., and . ICML, volume 162 of Proceedings of Machine Learning Research, page 7531-7549. PMLR, (2022)LocalNewton: Reducing communication rounds for distributed learning., , , , , and . UAI, volume 161 of Proceedings of Machine Learning Research, page 632-642. AUAI Press, (2021)Multi-agent Heterogeneous Stochastic Linear Bandits., , and . ECML/PKDD (4), volume 13716 of Lecture Notes in Computer Science, page 300-316. Springer, (2022)Distributed Newton Can Communicate Less and Resist Byzantine Workers., , and . NeurIPS, (2020)Multi-robot Cooperative Box-pushing problem using Multi-objective Particle Swarm Optimization Technique, , , , and . CoRR, (2012)Collaborative Learning and Personalization in Multi-Agent Stochastic Linear Bandits., , and . CoRR, (2021)Model Selection in Reinforcement Learning with General Function Approximations., and . ECML/PKDD (4), volume 13716 of Lecture Notes in Computer Science, page 148-164. Springer, (2022)Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits., , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 1396-1404. PMLR, (2021)Communication Efficient and Byzantine Tolerant Distributed Learning., , , , and . ISIT, page 2545-2550. IEEE, (2020)