Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, etc. Although the individual agents can be programmed in advance, many tasks require that they learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. This paper gives a survey of multi-agent reinforcement learning, starting with a review of the different viewpoints on the learning goal, which is a central issue in the field. Two generic goals are distinguished: stability of the learning dynamics, and adaptation to the other agents' dynamic behavior . The focus on one of these goals, or a combination of both, leads to a categorization of the methods and approaches in the field. The challenges and benefits of multi-agent reinforcement learning are outlined along with open issues and future research directions.