Reinforcement Learning with Augmented Data

[2004.14990] Reinforcement Learning with Augmented Data - arXiv

Title:Reinforcement Learning with Augmented Data ... Abstract:Learning from visual observations is a fundamental yet challenging problem in ...

To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms. We perform the ...

Reinforcement Learning with Augmented Data - Misha Laskin

To this end, we present RAD: Reinforcement Learning with Augmented Data, a simple plug-and-play module that can enhance any RL algorithm. We ...

MishaLaskin/rad: RAD: Reinforcement Learning with Augmented Data

RAD: Reinforcement Learning with Augmented Data . Contribute to MishaLaskin/rad development by creating an account on GitHub.

Reinforcement learning with augmented data - ACM Digital Library

We show that augmentations such as random translate, crop, color jitter, patch cutout, random convolutions, and amplitude scale can enable simple RL algorithms ...

Data Augmentation for RL

Would contrastive loss (aux loss) ever help improve policy performance, or just speed up convergence? Decoupling Representation Learning from Reinforcement ...

Reinforcement Learning with Augmented Data (RAD) - GitHub

RAD: Reinforcement Learning with Augmented Data (code for state augmentation) - pokaxpoka/rad_openaigym.

Data-Efficient Reinforcement Learning with Data Augmented ...

The model adds to the observations before being stored in the agent's memory, causing the agent to use the same logic and take the same action in comparable ...

Reinforcement Learning with Augmented Data - ResearchGate

Download Citation | Reinforcement Learning with Augmented Data | Learning from visual observations is a fundamental yet challenging problem ...

Reinforcement Learning with Augmentation Invariant Representation

Data augmentation has been proven as an effective measure to improve general- ization performance in reinforcement learning (RL). However, recent approaches.

Reinforcement Learning with Augmented ... - Review for NeurIPS paper

RAD can help learning the representation in reinforcement learning, which might be difficult before. This paper also systematically studies data augmentation to ...

Reinforcement Learning with Augmented Data - Semantic Scholar

It is shown that augmentations such as random translate, crop, color jitter, patch cutout, random convolutions, and amplitude scale can enable simple RL ...

[D] Paper Analysis - Reinforcement Learning with Augmented Data

https://youtu.be/to7vCdkLi4s This ONE SIMPLE TRICK can take a vanilla RL algorithm to achieve state-of-the-art. What is it? Simply augment ...

Revisiting Data Augmentation in Deep Reinforcement Learning

Various data augmentation techniques have been recently proposed in image-based deep reinforcement learning (DRL).

A Comprehensive Survey of Data Augmentation in Visual ... - arXiv

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

Reinforcement Learning with Augmented Data (Paper Explained)

This ONE SIMPLE TRICK can take a vanilla RL algorithm to achieve state-of-the-art. What is it? Simply augment your training data before ...

Generalization in Reinforcement Learning by Soft Data Augmentation

Specifically, SODA imposes a soft constraint on the encoder that aims to maximize the mutual information between latent representations of augmented and non- ...

Reinforcement Learning with Augmented Data - Connected Papers

Hence, using model-based methods to improve plant performance is not trivial. In this paper, an offline and model-free reinforcement ...

RLJ · Guided Data Augmentation for Offline Reinforcement Learning ...

In offline reinforcement learning (RL), an RL agent learns to solve a task using only a fixed dataset of previously collected data. While offline RL has been ...

Selective Data Augmentation for Improving the Performance of ...

This study proposes a new data augmentation technique for offline reinforcement learning (RL). Rather than randomly choosing data points to carry out the ...