Luketina, J;
Nardelli, N;
Farquhar, G;
Foerster, J;
Andreas, J;
Grefenstette, E;
Whiteson, S;
(2019)
A Survey of Reinforcement Learning Informed by Natural Language.
In: Eiter, Thomas and Kraus, Sarit, (eds.)
Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19).
(pp. pp. 6309-6317).
AAAI Press (Association for the Advancement of Artificial Intelligence): Palo Alto, CA, USA.
Preview |
Text
_NLP4RL (5).pdf - Accepted Version Download (200kB) | Preview |
Abstract
To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it to the task at hand. Recent advances in representation learning for language make it possible to build models that acquire world knowledge from text corpora and integrate this knowledge into downstream decision making problems. We thus argue that the time is right to investigate a tight integration of natural language understanding into RL in particular. We survey the state of the field, including work on instruction following, text games, and learning from textual domain knowledge. Finally, we call for the development of new environments as well as further investigation into the potential uses of recent Natural Language Processing (NLP) techniques for such tasks. Language Processing (NLP) methods for such tasks.
| Type: | Proceedings paper |
|---|---|
| Title: | A Survey of Reinforcement Learning Informed by Natural Language |
| Event: | 28th International Joint Conference on Artificial Intelligence (IJCAI-19), 10-16 August 2019, Macao, China |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.24963/ijcai.2019/880 |
| Publisher version: | https://doi.org/10.24963/ijcai.2019/880 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Machine Learning: Reinforcement Learning Natural Language Processing: NLP Applications and Tools Machine Learning: Transfer, Adaptation, Multi-task Learning Machine Learning: Deep Learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10074415 |
Archive Staff Only
 |
View Item |
