Multiagent learning in the presence of memory-bounded agents

In recent years, great strides have been made towards creating autonomous agents that can learn via interaction with their environment. When considering just an individual agent, it is often appropriate to model the world as being stationary, meaning that the same action from the same state will always yield the same (possibly stochastic) effects. However, in the presence of other independent agents, the environment is not stationary: an action's effects may depend on the actions of the other agents. This non-stationarity poses the primary challenge of multiagent learning and comprises the main reason that it is best considered distinctly from single agent learning. The multiagent learning problem is often studied in the stylized settings provided by repeated matrix games. The goal of this article is to introduce a novel multiagent learning algorithm for such a setting, called Convergence with Model Learning and Safety (or CMLeS), that achieves a new set of objectives which have not been previously achieved. Specifically, CMLeS is the first multiagent learning algorithm to achieve the following three objectives: (1) converges to following a Nash equilibrium joint-policy in self-play; (2) achieves close to the best response when interacting with a set of memory-bounded agents whose memory size is upper bounded by a known value; and (3) ensures an individual return that is very close to its security value when interacting with any other set of agents. Our presentation of CMLeS is backed by a rigorous theoretical analysis, including an analysis of sample complexity wherever applicable.