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

Understand

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.