Multi-agent reinforcement learning algorithm to handle beliefs of other agents' policies and embedded beliefs

By Takaki Makino, Kazuyuki Aihara. 2006

18 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Safety Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shimon Whiteson
2. Wendelin Böhmer
3. Christian Schröder de Witt
4. Jakob N. Foerster
5. Philip H. S. Torr
6. Gregory Farquhar
7. Chongjie Zhang
8. Tabish Rashid

In Management Science and Operations Research

1. Shimon Whiteson
2. Wendelin Böhmer
3. Christian Schröder de Witt
4. Gregory Farquhar
5. Jakob N. Foerster
6. Philip H. S. Torr
7. Filippos Christianos
8. Stefano V. Albrecht

In Safety Research

1. Shimon Whiteson
2. Christian Schröder de Witt
3. Jakob N. Foerster

Citations in the corpus

• Multi-Agent Common Knowledge Reinforcement Learning (2018, background): “ervation. Panella and Gmytrasiewicz (2017) treat the behaviours of teammates as a stochastic process and maintain beliefs over these in order to learn how to coordinate with previously unseen agents. Makino and Aihara (2006) develop an algorithm that reasons over the beliefs over policies of other a”