A Cooperative Benchmark: Announcing the Hanabi Learning Environment

Sun, 03 Feb 2019 00:00:00 +0000

Today, as part of a DeepMind / Google Brain team collaboration, we’re releasing the Hanabi Learning Environment (code and paper), a research platform for multiagent learning and emergent communication based on the popular card game Hanabi. The HLE provides an interface for AI agents to play the game, and comes packaged with a learning agent based on the Dopamine framework. The platform’s name echoes that of the highly-successful Arcade Learning Environment.

Hanabi is a two- to five-player cooperative game designed by Antoine Bauza. It was a revelation at the 2012 Internationale Spieltage in Essen and went on to win Spiel des Jahres, the most prestigious prize for board games, in 2013. In Hanabi, players work together to build five card sequences, each of a different colour. What makes the game interesting is that players can see their teammates’ cards, but not their own. Communication happens in great part through “hint” moves, where one person tells another something about their cards so that they know what to play or discard. Because there is a limited number of hints that can be given, good players communicate strategically and make use of conventions, for example “discard your oldest card first”.

Multiagent on Marc G. Bellemare

A Cooperative Benchmark: Announcing the Hanabi Learning Environment