UNMAS: Multi-Agent Reinforcement Learning for Unshaped Cooperative Scenarios

TL;DR

This paper introduces UNMAS, a novel multi-agent reinforcement learning method that adapts to changing numbers of agents and action sets, demonstrating superior performance in complex StarCraft II scenarios.

Contribution

UNMAS is the first method to effectively handle unshaped multi-agent scenarios with dynamic agent counts and action sets, using self-weighting mixing networks and dual-stream individual action-value networks.

Findings

UNMAS outperforms state-of-the-art MARL algorithms in StarCraft II scenarios.

UNMAS achieves the highest winning rates, especially in complex scenarios.

Agents learn effective cooperative behaviors where others fail.

Abstract

Multi-agent reinforcement learning methods such as VDN, QMIX, and QTRAN that adopt centralized training with decentralized execution (CTDE) framework have shown promising results in cooperation and competition. However, in some multi-agent scenarios, the number of agents and the size of action set actually vary over time. We call these unshaped scenarios, and the methods mentioned above fail in performing satisfyingly. In this paper, we propose a new method called Unshaped Networks for Multi-Agent Systems (UNMAS) that adapts to the number and size changes in multi-agent systems. We propose the self-weighting mixing network to factorize the joint action-value. Its adaption to the change in agent number is attributed to the nonlinear mapping from each-agent Q value to the joint action-value with individual weights. Besides, in order to address the change in action set, each agent constructs an individual action-value network that is composed of two streams to evaluate the constant environment-oriented subset and the varying unit-oriented subset. We evaluate UNMAS on various StarCraft II micro-management scenarios and compare the results with several state-of-the-art MARL algorithms. The superiority of UNMAS is demonstrated by its highest winning rates especially on the most difficult scenario 3s5z_vs_3s6z. The agents learn to perform effectively cooperative behaviors while other MARL algorithms fail in. Animated demonstrations and source code are provided in https://sites.google.com/view/unmas.