Since knowledge engineering is an inherently challenging and somewhat unbounded task, machine learning has been widely proposed as an alternative. In real world scenarios, we often need to explicitly model multiple agents, where intelligent agents act towards achieving goals either by coordinating with the other agents or by overseeing the opponents moves, if in a competitive context. We consider the knowledge acquisition problem where agents have knowledge about the world and other agents and then acquire new knowledge (both about the world as well as other agents) in service of answering queries. We propose a model of implicit learning, or more generally, learning to reason, which bypasses the intractable step of producing an explicit representation of the learned knowledge. We show that polynomial-time learnability results can be obtained when limited to knowledge bases and observations consisting of conjunctions of modal literals.