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MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control.

, , , , , and . NeurIPS, (2022)

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GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning., , , , , , and . CoRR, (2022)MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control., , , , , and . NeurIPS, (2022)End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning., , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 42 (6): 1317-1332 (2020)UnrealCV: Virtual Worlds for Computer Vision., , , , , , and . ACM Multimedia, page 1221-1224. ACM, (2017)AD-VAT: An Asymmetric Dueling mechanism for learning Visual Active Tracking., , , , and . ICLR (Poster), OpenReview.net, (2019)GFPose: Learning 3D Human Pose Prior with Gradient Fields., , , , , , and . CoRR, (2022)ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind., , , and . CoRR, (2021)RSPT: Reconstruct Surroundings and Predict Trajectories for Generalizable Active Object Tracking., , , , and . CoRR, (2023)CRAVES: Controlling Robotic Arm with a Vision-based Economic System., , , , , and . CoRR, (2018)Learning Semantic-Agnostic and Spatial-Aware Representation for Generalizable Visual-Audio Navigation., , , , , , and . CoRR, (2023)