We present Dedale, an environment for studying multi-agents coordination, learning and decision-making problems under realistic hypotheses. Dedale avoids the 8 fallacies of MAS in which all previous testbed fall and offers open, dynamic, asynchronous and partially observable environments. Highly parametrizable, Dedale allows to tackle either cooperative or competitive exploration, patrolling, pickup and delivery, trea-sure(s) or agent(s) hunt problems with teams from one to dozens of heterogeneous agents in discrete or continuous environments. The variety of modelable multi-agents problems associated with the possibility to create a peer-to-peer network of Dedale's environments makes us believe Dedale beeing able to become a unifying environment for both MAS research and teaching communities in their goal to work and evaluate their proposals under real-life hypotheses. Feedback from more than 150 early-users comfort us in this perspective
