Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach

TL;DR

This paper introduces an asynchronous, option-based multi-agent policy gradient method that employs a conditional reasoning approach to handle asynchronous option execution, improving policy learning in complex multi-agent tasks.

Contribution

It proposes a novel conditional reasoning framework for asynchronous option execution in multi-agent policy gradients, enhancing learning efficiency in complex environments.

Findings

Effective in complex multi-agent cooperative tasks

Handles asynchronous option execution successfully

Improves policy gradient estimation in high-dimensional spaces

Abstract

Cooperative multi-agent problems often require coordination between agents, which can be achieved through a centralized policy that considers the global state. Multi-agent policy gradient (MAPG) methods are commonly used to learn such policies, but they are often limited to problems with low-level action spaces. In complex problems with large state and action spaces, it is advantageous to extend MAPG methods to use higher-level actions, also known as options, to improve the policy search efficiency. However, multi-robot option executions are often asynchronous, that is, agents may select and complete their options at different time steps. This makes it difficult for MAPG methods to derive a centralized policy and evaluate its gradient, as centralized policy always select new options at the same time. In this work, we propose a novel, conditional reasoning approach to address this problem and demonstrate its effectiveness on representative option-based multi-agent cooperative tasks through empirical validation. Find code and videos at: \href{https://sites.google.com/view/mahrlsupp/}{https://sites.google.com/view/mahrlsupp/}