The Multi-Agent Reinforcement Learning in Malm\"O (MARL\"O) Competition
  Learning in multi-agent scenarios is a fruitful research direction, but
current approaches still show scalability problems in multiple games with
general reward settings and different opponent types. The Multi-Agent
Reinforcement Learning in Malm\"O (MARL\"O) competition is a new challenge that
proposes research in this domain using multiple 3D games. The goal of this
contest is to foster research in general agents that can learn across different
games and opponent types, proposing a challenge as a milestone in the direction
of Artificial General Intelligence.
