Multi-Agent Reinforcement Learning

Standard Reinforcement Learning studies how to build computational agents that can learn how to make decisions from interaction from their environment alone, even without a prior understanding of how that that environment works. This field is closely connected with human and animal learning and uses the idea of rewards obtained implicitely from the environment or explicitely from a trainer.

The field of Multi-Agent Reinforcement Learning has been growing steadily interest and complexity in recent years. This is RL in the more complex situation where there are other agents in the environment to interact with who also impact the reward obtained by our learning agent. Now these other agents could be teammates, oponents or neutral strangers and the learning agent might interact directly or indirectly with them. Game Theory is the study of a particular subset of this problem where the domain is generally well understood and while there may be hidden information which agents may not have access to, the agents do not need to learn about the environment itself in order to take actions.