# Analysing Factorizations of Action-Value Networks for Cooperative Multi-Agent Reinforcement Learning

## TL;DR

This paper empirically investigates how different neural network architectures learn value functions in cooperative multi-agent reinforcement learning, revealing insights into their capabilities and limitations in simplified game settings.

## Contribution

It provides a systematic analysis of the learning power of various network architectures on one-shot games, highlighting factors affecting performance and representation.

## Key findings

- Certain architectures better capture value functions in complex coordination scenarios
- Sparsity of values can hinder learning effectiveness
- Tight coordination requirements pose challenges for neural networks

## Abstract

Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. However, given the lack of theoretical insight, it remains unclear what the employed neural networks are learning, or how we should enhance their learning power to address the problems on which they fail. In this work, we empirically investigate the learning power of various network architectures on a series of one-shot games. Despite their simplicity, these games capture many of the crucial problems that arise in the multi-agent setting, such as an exponential number of joint actions or the lack of an explicit coordination mechanism. Our results extend those in [4] and quantify how well various approaches can represent the requisite value functions, and help us identify the reasons that can impede good performance, like sparsity of the values or too tight coordination requirements.