Is Centralized Training with Decentralized Execution Framework
  Centralized Enough for MARL?
Yihe Zhou Shunyu Liu Yunpeng Qing Kaixuan Chen Tongya Zheng Yanhao Huang Jie Song Mingli Song
Centralized Training with Decentralized Execution (CTDE) has recently emerged
as a popular framework for cooperative Multi-Agent Reinforcement Learning
(MARL), where agents can use additional global state information to guide
training in a centralized way and make their own decisions only based on
decentralized local policies. Despite the encouraging results achieved, CTDE
makes an independence assumption on agent policies, which limits agents to
adopt global cooperative information from each other during centralized
training. Therefore, we argue that existing CTDE methods cannot fully utilize
global information for training, leading to an inefficient joint-policy
exploration and even suboptimal results. In this paper, we introduce a novel
Centralized Advising and Decentralized Pruning (CADP) framework for multi-agent
reinforcement learning, that not only enables an efficacious message exchange
among agents during training but also guarantees the independent policies for
execution. Firstly, CADP endows agents the explicit communication channel to
seek and take advices from different agents for more centralized training. To
further ensure the decentralized execution, we propose a smooth model pruning
mechanism to progressively constraint the agent communication into a closed one
without degradation in agent cooperation capability. Empirical evaluations on
StarCraft II micromanagement and Google Research Football benchmarks
demonstrate that the proposed framework achieves superior performance compared
with the state-of-the-art counterparts. Our code will be made publicly
available.