What is Rrl?
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RRL, or Resnet as representation for Reinforcement Learning, is a method proposed to enable robots to autonomously learn behaviors directly from proprioceptive inputs like onboard cameras and joint encoders . This approach combines features extracted from pre-trained Resnet into the standard reinforcement learning pipeline, allowing the robot to learn complex behaviors effectively from visual inputs . RRL has shown promising results in tasks such as dexterous manipulation, where traditional methods struggle, by producing contact-rich behaviors . The appeal of RRL lies in its simplicity, merging advancements from Representation Learning, Imitation Learning, and Reinforcement Learning fields to achieve efficient learning directly from visual inputs in high-dimensional and partially observable environments .
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Open access•Proceedings Article 18 Jul 2021 | RRL (Resnet as representation for Reinforcement Learning) is a method that integrates pre-trained Resnet features into the standard reinforcement learning pipeline, enabling learning complex behaviors directly from visual inputs. |
Journal Article 1 Citations | RRL stands for Regional Rotation Layer, a module proposed in CNNs to incorporate rotation invariance without adding complexity or learnable parameters, enhancing performance on rotated testing sets. |
| RRL (Regional Rotate Layer) is a module proposed for CNNs to incorporate rotation invariance into feature extraction layers without adding parameters, enhancing model performance significantly. | |
| Not addressed in the paper. | |
| Not addressed in the paper. |
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