can good learners always compensate for poor learners?
Can a good learner compensate for a poor learner when paired in a coordination game? Previous work presented an example where a special learning algorithm (FMQ) is capable of doing just that when paired with a specific less capable algorithm even in games which stump the poorer algorithm when paired with itself. We argue that this result is not general. We give a straightforward extension to the coordination game in which FMQ cannot compensate for the lesser algorithm. We also provide other problematic pairings, and argue that another high-quality algorithm cannot do so either.
multiagent systems cooperative games reinforcement learning