Unsupervised Representation Learning in Deep Reinforcement Learning: A Review

08/27/2022
by   Nicolò Botteghi, et al.
6

This review addresses the problem of learning abstract representations of the measurement data in the context of Deep Reinforcement Learning (DRL). While the data are often ambiguous, high-dimensional, and complex to interpret, many dynamical systems can be effectively described by a low-dimensional set of state variables. Discovering these state variables from the data is a crucial aspect for improving the data efficiency, robustness and generalization of DRL methods, tackling the curse of dimensionality, and bringing interpretability and insights into black-box DRL. This review provides a comprehensive and complete overview of unsupervised representation learning in DRL by describing the main Deep Learning tools used for learning representations of the world, providing a systematic view of the method and principles, summarizing applications, benchmarks and evaluation strategies, and discussing open challenges and future directions.

READ FULL TEXT

page 9

page 28

page 34

research
07/05/2022

Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications

The use of Deep Reinforcement Learning (DRL) schemes has increased drama...
research
10/13/2020

Deep Reinforcement Learning and Transportation Research: A Comprehensive Review

Deep reinforcement learning (DRL) is an emerging methodology that is tra...
research
02/17/2022

A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization

Deep Reinforcement Learning (DRL) aims to create intelligent agents that...
research
11/21/2022

Disentangled Representation Learning

Disentangled Representation Learning (DRL) aims to learn a model capable...
research
03/18/2019

Deep Reinforcement Learning with Decorrelation

Learning an effective representation for high-dimensional data is a chal...
research
05/17/2019

Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem

As global greenhouse gas emissions continue to rise, the use of stratosp...
research
12/01/2020

Assessing and Accelerating Coverage in Deep Reinforcement Learning

Current deep reinforcement learning (DRL) algorithms utilize randomness ...

Please sign up or login with your details

Forgot password? Click here to reset