_:n62a0251cce17434fab2843caf5fa4858xb49
<http://schema.org/description>
"Information sharing is key in building team cognition and enables\ncoordination and cooperation. High-performing human teams also benefit from\nacting strategically with hierarchical levels of iterated communication and\nrationalizability, meaning a human agent can reason about the actions of their\nteammates in their decision-making. Yet, the majority of prior work in\nMulti-Agent Reinforcement Learning (MARL) does not support iterated\nrationalizability and only encourage inter-agent communication, resulting in a\nsuboptimal equilibrium cooperation strategy. In this work, we show that\nreformulating an agent's policy to be conditional on the policies of its\nneighboring teammates inherently maximizes Mutual Information (MI) lower-bound\nwhen optimizing under Policy Gradient (PG). Building on the idea of\ndecision-making under bounded rationality and cognitive hierarchy theory, we\nshow that our modified PG approach not only maximizes local agent rewards but\nalso implicitly reasons about MI between agents without the need for any\nexplicit ad-hoc regularization terms. Our approach, InfoPG, outperforms\nbaselines in learning emergent collaborative behaviors and sets the\nstate-of-the-art in decentralized cooperative MARL tasks. Our experiments\nvalidate the utility of InfoPG by achieving higher sample efficiency and\nsignificantly larger cumulative reward in several complex cooperative\nmulti-agent domains."
<https://deepai.org/profile/esmaeil-seraj>