Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming

01/20/2022
by   Sachin Konan, et al.
0

Information sharing is key in building team cognition and enables coordination and cooperation. High-performing human teams also benefit from acting strategically with hierarchical levels of iterated communication and rationalizability, meaning a human agent can reason about the actions of their teammates in their decision-making. Yet, the majority of prior work in Multi-Agent Reinforcement Learning (MARL) does not support iterated rationalizability and only encourage inter-agent communication, resulting in a suboptimal equilibrium cooperation strategy. In this work, we show that reformulating an agent's policy to be conditional on the policies of its neighboring teammates inherently maximizes Mutual Information (MI) lower-bound when optimizing under Policy Gradient (PG). Building on the idea of decision-making under bounded rationality and cognitive hierarchy theory, we show that our modified PG approach not only maximizes local agent rewards but also implicitly reasons about MI between agents without the need for any explicit ad-hoc regularization terms. Our approach, InfoPG, outperforms baselines in learning emergent collaborative behaviors and sets the state-of-the-art in decentralized cooperative MARL tasks. Our experiments validate the utility of InfoPG by achieving higher sample efficiency and significantly larger cumulative reward in several complex cooperative multi-agent domains.

READ FULL TEXT

page 22

page 26

research
08/06/2018

Learning to Share and Hide Intentions using Information Regularization

Learning to cooperate with friends and compete with foes is a key compon...
research
06/04/2020

A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning

In this paper, we propose a maximum mutual information (MMI) framework f...
research
02/18/2023

Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help

Multi-agent reinforcement learning (MARL) has achieved great progress in...
research
01/28/2022

FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems

Decentralized cooperation in partially-observable multi-agent systems re...
research
03/28/2023

A Hierarchical Game-Theoretic Decision-Making for Cooperative Multi-Agent Systems Under the Presence of Adversarial Agents

Underlying relationships among Multi-Agent Systems (MAS) in hazardous sc...
research
01/17/2022

Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss

In a cooperative multiagent system, a collection of agents executes a jo...
research
02/02/2022

Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic

Building autonomous vehicles (AVs) is a complex problem, but enabling th...

Please sign up or login with your details

Forgot password? Click here to reset