# Decentralized Learning for Optimality in Stochastic Dynamic Teams and Games with Local Control and Global State Information

## TL;DR

This paper introduces a novel decentralized learning algorithm that guarantees convergence to team optimal policies in stochastic dynamic teams and games, using only local information and independent learning agents.

## Contribution

It presents the first formal convergence guarantees for independent learners achieving team optimality in stochastic dynamic teams and common interest games.

## Key findings

- Algorithm guarantees convergence to team optimal policies.
- Agents only use local controls, costs, and global state information.
- First formal proof of independent learners achieving team optimality.

## Abstract

Stochastic dynamic teams and games are rich models for decentralized systems and challenging testing grounds for multi-agent learning. Previous work that guaranteed team optimality assumed stateless dynamics, or an explicit coordination mechanism, or joint-control sharing. In this paper, we present an algorithm with guarantees of convergence to team optimal policies in teams and common interest games. The algorithm is a two-timescale method that uses a variant of Q-learning on the finer timescale to perform policy evaluation while exploring the policy space on the coarser timescale. Agents following this algorithm are "independent learners": they use only local controls, local cost realizations, and global state information, without access to controls of other agents. The results presented here are the first, to our knowledge, to give formal guarantees of convergence to team optimality using independent learners in stochastic dynamic teams and common interest games.