Reinforcement Learning with Augmentation Invariant Representation

In this work, we present RAIR: Reinforcement learning with Augmentation Invariant Representation that disen- tangles the representation learning task from the ...

Data augmentation has been proven as an effective measure to improve generalization performance in reinforcement learning (RL).

Revisiting the Critical Factors of Augmentation-Invariant ... - arXiv

Abstract:We focus on better understanding the critical factors of augmentation-invariant representation learning. ... Machine Learning (cs.

Reinforcement Learning with Euclidean Data Augmentation for State ...

... representation and therefore it's already translation-invariant. Reflections require gait symmetry (e.g., the left leg has the same length ...

Invariant Representation Learning for Generalizable Imitation

Invariant Representation Learning ... ing invariant representations for reinforcement learning without reconstruction. ... aware adversarial data augmentation. In ...

Invariant representation learning for robust deep networks

In. International Conference on Machine Learning (ICML), pages 410–418, 2013. ... AutoAugment: Learning augmentation policies from data. CoRR, abs/1805.09501 ...

Revisiting the Critical Factors of Augmentation-Invariant ... - ECVA

idea that training details determine the characteristics of learned representations in augmentation-invariant representation learning. ... Proceedings of Machine ...

Augmentation Invariant Training - NICS-EFC

Data augmentation is widely used to reduce the general- ization error of neural networks in many machine learning tasks. Commonly used data augmentation ...

Invariant Action Effect Model for Reinforcement Learning

Previous contrastive-based approaches calculate the simi- larity g by treating its augmented data (Srinivas, Laskin, and ... Learning Invariant Representations ...

Toward Learning Robust and Invariant Representations with ...

Data augmentation has been proven to be an effective technique for developing machine learning models that are robust to known classes of ...

Task-Aware Lipschitz Data Augmentation for Visual Reinforcement ...

Learn- ing invariant representations for reinforcement learning without reconstruction. arXiv preprint arXiv:2006.10742,. 2020. [Zhu et al., 2017] Yuke Zhu ...

Learning Domain Invariant Representations in Goal-conditioned ...

Deep Reinforcement Learning (RL) is successful in solving many complex Markov. Decision Processes (MDPs) problems. However, agents often face unanticipated.

fuyw/RepL4RL: Representation Learning for RL - GitHub

[ICLR' 21][DBC] Learning invariant representations for reinforcement learning without reconstruction (Code) ... Augmentation Is All You Need: Regularizing Deep ...

Invariant Representation Learning for Generalizable Imitation

Automatic data augmentation for generalization in reinforcement learning. Advances in Neural Information Processing Systems, 2021. Improving ...

Invariant representation learning for robust deep networks

... augmentation. Rather than only presenting original and ... reinforcement learning (Bandits), adaptive experimentation, causal inference, data engineering.

Robust Representation Learning by Clustering with Bisimulation ...

Automatic data augmentation for general- ization in deep reinforcement learning ... Learning invariant representations for reinforce- ment learning without ...

Architectural and algorithmic strategies for generalizable deep ...

Second, to improve resilience to potential observational variations, RL using augmentation-invariant representation is introduced, learning latent ...

Revisiting the Critical Factors of Augmentation-Invariant ...

We focus on better understanding the critical factors of augmentation-invariant representation learning. We revisit MoCo v2 and BYOL and try to prove the ...

Automatic Data Augmentation for Generalization in Reinforcement ...

... augmentations at different stages during the training of the agent's ... Learning invariant representations for reinforcement learning without reconstruction.

How to Learn Domain-Invariant Representations for Visual ... - IJCAI

Data augmentation is widely adopted in visual reinforce- ment learning to improve sample efficiency and generaliza- tion. RAD [Laskin et al., 2020b] is the ...