SATF: A Scalable Attentive Transfer Framework for Efficient Multiagent Reinforcement Learning.

Abstract

It is challenging to train an efficient learning procedure with multiagent reinforcement learning (MARL) when the number of agents increases as the observation space exponentially expands, especially in large-scale multiagent systems. In this article, we proposed a scalable attentive transfer framework (SATF) for efficient MARL, which achieved goals faster and more accurately in homogeneous and heterogeneous combat tasks by transferring learned knowledge from a small number of agents (4) to a large number of agents (up to 64). To reduce and align the dimensionality of the observed state variations caused by increasing numbers of agents, the proposed SATF deployed a novel state representation network with a self-attention mechanism, known as dynamic observation representation network (DorNet), to extract the dominant observed information with excellent cost-effectiveness. The experiments on the MAgent platform showed that the SATF outperformed the distributed MARL (independent Q-learning (IQL) and A2C) in task sequences from 8 to 64 agents. The experiments on StarCraft II showed that the SATF demonstrated superior performance relative to the centralized training with decentralized execution MARL (QMIX) by presenting shorter training steps, achieving a desired win rate of up to approximately 90% when increasing the number of agents from 4 to 32. The findings of our study showed the great potential for enhancing the efficiency of MARL training in large-scale agent combat missions.