Research on Multi-Agent Communication and Collaborative Decision-Making
  Based on Deep Reinforcement Learning
Zeng Da
In a multi-agent environment, In order to overcome and alleviate the
non-stationarity of the multi-agent environment, the mainstream method is to
adopt the framework of Centralized Training Decentralized Execution (CTDE).
This thesis is based on the framework of CTDE, and studies the cooperative
decision-making of multi-agent based on the Multi-Agent Proximal Policy
Optimization (MAPPO) algorithm for multi-agent proximal policy optimization. In
order to alleviate the non-stationarity of the multi-agent environment, a
multi-agent communication mechanism based on weight scheduling and attention
module is introduced. Different agents can alleviate the non-stationarity
caused by local observations through information exchange between agents,
assisting in the collaborative decision-making of agents. The specific method
is to introduce a communication module in the policy network part. The
communication module is composed of a weight generator, a weight scheduler, a
message encoder, a message pool and an attention module. Among them, the weight
generator and weight scheduler will generate weights as the selection basis for
communication, the message encoder is used to compress and encode communication
information, the message pool is used to store communication messages, and the
attention module realizes the interactive processing of the agent's own
information and communication information. This thesis proposes a Multi-Agent
Communication and Global Information Optimization Proximal Policy
Optimization(MCGOPPO)algorithm, and conducted experiments in the SMAC and the
MPE. The experimental results show that the improvement has achieved certain
effects, which can better alleviate the non-stationarity of the multi-agent
environment, and improve the collaborative decision-making ability among the
agents.