RL with Self-Supervised 3D
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Visual Reinforcement Learning with Self-Supervised 3D Representation
Visual Reinforcement Learning with Self-Supervised 3D Representations 1Shanghai Jiao Tong University, 2UC San Diego *Equal Contribution RA-L 2023 & IROS 2023 Abstract A prominent approach to visual Reinforcement Learning (RL) is to learn an internal state representation using self-supervised methods, which has the potential benefit of improved sample-efficiency and generalization through additional learning signal and inductive biases. However, while the real world is inherently 3D, prior efforts have largely been focused on leveraging 2D computer vision techniques as auxiliary…
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