Exploratory State Representation Learning
Not having access to compact and meaningful representations is known to significantly increase the complexity of reinforcement learning (RL). For this reason, it can be useful to perform state representation learning (SRL) before tackling RL tasks. However, obtaining a good state representation can...
Main Authors: | Astrid Merckling, Nicolas Perrin-Gilbert, Alex Coninx, Stéphane Doncieux |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2022-02-01
|
Series: | Frontiers in Robotics and AI |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2022.762051/full |
Similar Items
-
Transformer Decoder-Based Enhanced Exploration Method to Alleviate Initial Exploration Problems in Reinforcement Learning
by: Dohyun Kyoung, et al.
Published: (2023-08-01) -
Open-Ended Learning: A Conceptual Framework Based on Representational Redescription
by: Stephane Doncieux, et al.
Published: (2018-09-01) -
An Experimental Study on State Representation Extraction for Vision-Based Deep Reinforcement Learning
by: Junkai Ren, et al.
Published: (2021-11-01) -
Research Progress on Vision–Language Multimodal Pretraining Model Technology
by: Huansha Wang, et al.
Published: (2022-10-01) -
Practical Algorithmic Trading Using State Representation Learning and Imitative Reinforcement Learning
by: Deog-Yeong Park, et al.
Published: (2021-01-01)