Reinforcement Learning in Multi-agent Games: Open AI Gym Diplomacy Environment

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Diogo Cruz (Author) Other; José Aleixo Cruz (Author) Other; Henrique Lopes Cardoso (Author) FEUP

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 49-60

19th EPIA Conference on Artificial Intelligence, EPIA 2019

3 September 2019 through 6 September 2019

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00R-4GM

DOI: 10.1007/978-3-030-30241-2_5

Abstract (EN): Reinforcement learning has been successfully applied to adversarial games, exhibiting its potential. However, most real-life scenarios also involve cooperation, in addition to competition. Using reinforcement learning in multi-agent cooperative games is, however, still mostly unexplored. In this paper, a reinforcement learning environment for the Diplomacy board game is presented, using the standard interface adopted by OpenAI Gym environments. Our main purpose is to enable straightforward comparison and reuse of existing reinforcement learning implementations when applied to cooperative games. As a proof-of-concept, we show preliminary results of reinforcement learning agents exploiting this environment. © Springer Nature Switzerland AG 2019.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12