  We develop model free PAC performance guarantees for multiple concurrent
MDPs, extending recent works where a single learner interacts with multiple
non-interacting agents in a noise free environment. Our framework allows noisy
and resource limited communication between agents, and develops novel PAC
guarantees in this extended setting. By allowing communication between the
agents themselves, we suggest improved PAC-exploration algorithms that can
overcome the communication noise and lead to improved sample complexity bounds.
We provide a theoretically motivated algorithm that optimally combines
information from the resource limited agents, thereby analyzing the interaction
between noise and communication constraints that are ubiquitous in real-world
systems. We present empirical results for a simple task that supports our
theoretical formulations and improve upon naive information fusion methods.
