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Domain generalization: A survey
Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …
challenging for machines to reproduce. This is because most learning algorithms strongly …
A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures
Motion planning is critical to realize the autonomous operation of mobile robots. As the
complexity and randomness of robot application scenarios increase, the planning capability …
complexity and randomness of robot application scenarios increase, the planning capability …
The many faces of robustness: A critical analysis of out-of-distribution generalization
We introduce four new real-world distribution shift datasets consisting of changes in image
style, image blurriness, geographic location, camera operation, and more. With our new …
style, image blurriness, geographic location, camera operation, and more. With our new …
A survey of zero-shot generalisation in deep reinforcement learning
The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …
produce RL algorithms whose policies generalise well to novel unseen situations at …
Reinforcement learning with augmented data
Learning from visual observations is a fundamental yet challenging problem in
Reinforcement Learning (RL). Although algorithmic advances combined with convolutional …
Reinforcement Learning (RL). Although algorithmic advances combined with convolutional …
[PDF][PDF] A survey of reinforcement learning from human feedback
Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …
(RL) that learns from human feedback instead of relying on an engineered reward function …
Leveraging procedural generation to benchmark reinforcement learning
Abstract We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like
environments designed to benchmark both sample efficiency and generalization in …
environments designed to benchmark both sample efficiency and generalization in …
Stabilizing deep q-learning with convnets and vision transformers under data augmentation
While agents trained by Reinforcement Learning (RL) can solve increasingly challenging
tasks directly from visual observations, generalizing learned skills to novel environments …
tasks directly from visual observations, generalizing learned skills to novel environments …
Robust and generalizable visual representation learning via random convolutions
While successful for various computer vision tasks, deep neural networks have shown to be
vulnerable to texture style shifts and small perturbations to which humans are robust. In this …
vulnerable to texture style shifts and small perturbations to which humans are robust. In this …
Broaden your views for self-supervised video learning
Most successful self-supervised learning methods are trained to align the representations of
two independent views from the data. State-of-the-art methods in video are inspired by …
two independent views from the data. State-of-the-art methods in video are inspired by …