Multiagent Systems is the emerging subfield of Artificial Intelligence that aims to provide both principles for construction of complex systems involving multiple agents and mechanisms for coordination of independent agents’ behaviors. As of yet, there has been little work with lVIultiagent Systems that require real-time control in noisy environments. Because of the inherent complexity of this type of Multiagent System, Machine Learning is an interesting and promising area to merge with Multiagent Systems. Machine learning has the potential to provide robust mechanisms that leverage upon experience to equip agents with a large spectrum of behaviors, ranging from effective individual performance in a team, to collaborative achievement of independently and jointly set high-level goals in the presence of adversaries. Learning will also help agents adapt to unforeseen behaviors on the parts of other agents, through the use of on-line adaptive methods that may include explicit opponent modelling. My thesis will focus on learning in this particularly complex class of multiagent domains.