Reinforcement Learning with Augmented Data

RLJ · A Recipe for Unbounded Data Augmentation in Visual ...

By Abdulaziz Almuzairee, Nicklas Hansen, and Henrik I Christensen. Reinforcement Learning Journal, vol. 1, 2024, pp. 130–157. Presented at the Reinforcement ...

Feature Augmentation with Reinforcement Learning - Database Group

A machine learning (ML) model is only as good as the data (and features) that it is trained on. However, it is not uncommon that the training data (even from ...

A stable data-augmented reinforcement learning method with ...

A stable data-augmented reinforcement learning method with ensemble exploration and exploitation. Published: 28 July 2023. Volume 53, pages 24792–24803, ...

Automatic Data Augmentation for Reinforcement Learning | Restackio

Data augmentation plays a crucial role in enhancing the generalization capabilities of reinforcement learning (RL) models. By artificially ...

Memory-Augmented Reinforcement Learning for Image ... - Hal-Inria

We apply the same data augmentation techniques to obser- vations during this reachability network training phase. 2) Episodic Memory: Once we ...

DRL-Tomo: a deep reinforcement learning-based approach to ...

The key idea is to rely on deep reinforcement learning to generate augmented data for training neural networks. Unlike NeuTomography, which uses ...

Reinforcement Learning with Augmented Data - SlidesLive

About NeurIPS 2020. Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience ...

Revolutionizing Machine Learning with Generative Augmented ...

The basic idea is to use generative models to create augmented data and use this data to train reinforcement learning agents. By doing this ...

Using Data Augmentation Based Reinforcement Learning for Daily ...

In algorithmic trading, adequate training data set is key to making profits. However, stock trading data in units of a day can not meet the great demand for ...

Value Function Evaluation with Data Augmentation for Offline ...

Offline reinforcement learning, also known as batch reinforcement learning, aims to utilize pre-collected offline datasets for training ...

Reinforcement Learning based Data Augmentation for Noise Robust ...

Index Terms: speech emotion recognition, noise robustness, selective data augmentation, reinforcement learning. 1. Introduction. Speech emotion ...

ACAMDA: Improving Data Efficiency in Reinforcement Learning ...

Data augmentation plays a crucial role in improving the data efficiency of reinforcement learning (RL). However, the gen- eration of high-quality augmented data ...

What is data augmentation? - IBM

Data augmentation improves machine learning model optimization and generalization. In other words, data augmentation can reduce overfitting and ...

UC Berkeley researchers open-source RAD to improve any ...

A group of University of California, Berkeley researchers this week open-sourced Reinforcement Learning with Augmented Data (RAD).

A Comprehensive Survey of Data Augmentation in Visual ...

Visual reinforcement learning (RL), which makes decisions directly from high-dimensional visual inputs, has demonstrated significant ...

Text Data Augmentation for Deep Learning | Journal of Big Data

[3] were able to collect a dataset of 5.4K Too Long; Didn't Read (TLDR) summaries of 3.2K machine learning papers. This required employing 28 ...

Data Augmentation for Regularizing Learned World Models in ...

Recent successes in applying data augmentation to model-free reinforcement learning algorithms [2, 3] raised the question whether data ...

Data Augmentation for Machine Learning - Akkio

Data Augmentation For Machine Learning. Data augmentation is the process of modifying, or “augmenting” a dataset with additional data. This additional data can ...

Improving GNSS Positioning Using Deep Reinforcement Learning ...

... augmented data, leading to unstable training on the RL optimization process. To address this problem, we decouple the data augmentation from RL by strictly ...

similar - arxiv-sanity

In deep reinforcement learning (RL), data augmentation is widely considered ... augmented data under Euclidean transformations. With minimal ...