An Overview of Natural Language State Representation for Reinforcement Learning
By Brielen Madureira, David Schlangen. 2020
10 citations recorded by the source corpus.
A suitable state representation is a fundamental part of the learning process in Reinforcement Learning. In various tasks, the state can either be described by natural language or be natural language itself. This survey outlines the strategies used in the literature to build natural language state representations. We appeal for more linguistically interpretable and grounded representations, careful justification of design decisions and evaluation of the effectiveness of different approaches.
Subfields: Artificial Intelligence, Information Systems
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In Artificial Intelligence
- Michael Heck
- Carel van Niekerk
- Nurul Lubis
- Milica Gavsi'c
- Christian Geishauser
- Shutong Feng
- Hsien-Chin Lin
Citations in the corpus
- Dynamic Dialogue Policy for Continual Reinforcement Learning (2022, background): “267 to the success of any form of RL (Madureira and Schlangen, 2020).”
- Dynamic Dialogue Policy Transformer for Continual Reinforcement Learning (2022, background): “An appropriate choice of state representation is key to the success of any form of RL (Madureira and Schlangen, 2020).”