Learning to Communicate Implicitly By Actions

Understand

In situations where explicit communication is limited, human collaborators act by learning to: (i) infer meaning behind their partner's actions, and (ii) convey private information about the state to their partner implicitly through actions.

• The first component of this learning process has been well-studied in multi-agent systems, whereas the second --- which is equally crucial for successful collaboration --- has not.
• To mimic both components mentioned above, thereby completing the learning process, we introduce a novel algorithm: Policy Belief Learning (PBL).
• PBL uses a belief module to model the other agent's private information and a policy module to form a distribution over actions informed by the belief module.