Theory of Mind for Deep Reinforcement Learning in Hanabi
  The partially observable card game Hanabi has recently been proposed as a new
AI challenge problem due to its dependence on implicit communication
conventions and apparent necessity of theory of mind reasoning for efficient
play. In this work, we propose a mechanism for imbuing Reinforcement Learning
agents with a theory of mind to discover efficient cooperative strategies in
Hanabi. The primary contributions of this work are threefold: First, a formal
definition of a computationally tractable mechanism for computing hand
probabilities in Hanabi. Second, an extension to conventional Deep
Reinforcement Learning that introduces reasoning over finitely nested theory of
mind belief hierarchies. Finally, an intrinsic reward mechanism enabled by
theory of mind that incentivizes agents to share strategically relevant private
knowledge with their teammates. We demonstrate the utility of our algorithm
against Rainbow, a state-of-the-art Reinforcement Learning agent.
