From the abstract:

Multi-agent systems can be used to address problems in a variety of do- mains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must instead discover a solution on their own, using learning. A significant part of the research on multi-agent learn- ing concerns reinforcement learning techniques. This chapter reviews a representa- tive selection of Multi-Agent Reinforcement Learning (MARL) algorithms for fully cooperative, fully competitive, and more general (neither cooperative nor competi- tive) tasks. The benefits and challenges of MARL are described. A central challenge in the field is the formal statement of a multi-agent learning goal; this chapter re- views the learning goals proposed in the literature. The problem domains where MARL techniques have been applied are briefly discussed. Several MARL algo- rithms are applied to an illustrative example involving the coordinated transporta- tion of an object by two cooperative robots. In an outlook for the MARL field, a set of important open issues are identified, and promising research directions to address these issues are outlined.