CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning

TL;DR

This paper introduces CTDS, a novel framework for multi-agent reinforcement learning that uses a centralized teacher to improve decentralized student performance, enhancing global information utilization during training.

Contribution

The paper proposes a new CTDS framework that combines centralized teaching with decentralized execution, improving upon existing CTDE methods in MARL.

Findings

CTDS outperforms existing value-based MARL methods in StarCraft II tasks.

The framework effectively balances global observation use during training and decentralized inference.

Experimental results demonstrate improved learning efficiency and performance.

Abstract

Due to the partial observability and communication constraints in many multi-agent reinforcement learning (MARL) tasks, centralized training with decentralized execution (CTDE) has become one of the most widely used MARL paradigms. In CTDE, centralized information is dedicated to learning the allocation of the team reward with a mixing network, while the learning of individual Q-values is usually based on local observations. The insufficient utility of global observation will degrade performance in challenging environments. To this end, this work proposes a novel Centralized Teacher with Decentralized Student (CTDS) framework, which consists of a teacher model and a student model. Specifically, the teacher model allocates the team reward by learning individual Q-values conditioned on global observation, while the student model utilizes the partial observations to approximate the Q-values estimated by the teacher model. In this way, CTDS balances the full utilization of global observation during training and the feasibility of decentralized execution for online inference. Our CTDS framework is generic which is ready to be applied upon existing CTDE methods to boost their performance. We conduct experiments on a challenging set of StarCraft II micromanagement tasks to test the effectiveness of our method and the results show that CTDS outperforms the existing value-based MARL methods.