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MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control., , , , , и . NeurIPS, (2022)GFPose: Learning 3D Human Pose Prior with Gradient Fields., , , , , , и . CVPR, стр. 4800-4810. IEEE, (2023)CRAVES: Controlling Robotic Arm With a Vision-Based Economic System., , , , , и . CVPR, стр. 4214-4223. Computer Vision Foundation / IEEE, (2019)TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification., , , и . NeurIPS, (2022)Disentangling Disease-related Representation from Obscure for Disease Prediction., , , , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 22652-22664. PMLR, (2022)TarGF: Learning Target Gradient Field for Object Rearrangement., , , и . CoRR, (2022)RSPT: Reconstruct Surroundings and Predict Trajectory for Generalizable Active Object Tracking., , , , и . AAAI, стр. 3705-3714. AAAI Press, (2023)ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind., , , и . ICLR, OpenReview.net, (2022)Empowering Embodied Visual Tracking with Visual Foundation Models and Offline RL., , , , и . CoRR, (2024)Proactive Multi-Camera Collaboration for 3D Human Pose Estimation., , , , и . ICLR, OpenReview.net, (2023)